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Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
Open accessAbstract
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Overview
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TL;DR
The paper proposes anatomically constrained neural networks (ACNN), a generic end-to-end training strategy that uses a stacked convolutional autoencoder and T-L network to impose learnt anatomical shape priors on CNN predictions. It demonstrates improved cardiac MR segmentation, 3D ultrasound segmentation, MR super-resolution, and pathology classification over state-of-the-art baselines.
Key findings
- For cardiac cine-MR segmentation, ACNN-Seg improved endocardium Dice to .939 ± .017 versus .908 ± .021 for 2D-FCN, and myocardium Dice to .811 ± .027 versus .727 ± .046; mean surface distances were also lower (endocardium 1.37 ± 0.42 mm vs 2.07 ± 0.61 mm; myocardium 1.14 ± 0.22 mm vs 1.58 ± 0.44 mm), with p < 0.001 for Dice and mean distance.
- On the CETUS’14 3D-US benchmark, ACNN-Seg achieved end-diastole Dice .912 ± .023 versus .906 ± .026 for FCN and .894 ± .041 for BEAS, and end-systole Dice .873 ± .051 versus .872 ± .050 for FCN and .856 ± .057 for BEAS; Hausdorff distance at ED was 6.96 ± 1.75 mm versus 11.94 ± 9.46 mm for FCN.
- For cardiac MR super-resolution, ACNN-SR achieved SSIM .796 ± .041 versus .783 ± .046 for SR-CNN, clinician MOS scores of 4.36 ± 0.62 and 4.25 ± .68 versus 3.59 ± 1.05 and 3.85 ± .70, and inference time .06 s versus .29 s.
- For pathology classification, T-L codes gave 83.3% accuracy from ED phase and 91.6% with ED+ES, compared with PCA codes at 76.6% and 86.6%, using 10-fold cross-validation on 60 CMR sequences.
Methods
- ACNN adds a shape-regularisation loss to standard CNN training: segmentation uses cross-entropy plus a low-dimensional shape loss and weight decay, while super-resolution uses smooth ℓ1 reconstruction plus a global shape loss and weight decay. The regulariser is a stacked convolutional autoencoder and T-L network that learns 64-dimensional anatomical shape/label representations; it is used only during training, not inference.
- Data include the UK Digital Heart Project dataset of 1200 cine 2D stack and 3D HR cardiac MR image pairs, split into 900 training, 100 validation, and 200 testing for segmentation, and 1000 training / 200 testing for super-resolution; CETUS’14 with 45 3D+time ultrasound sequences from 15 healthy subjects and 30 patients, evaluated blinded on 30 test sequences; and ACDC MICCAI’17 with 20 cine stacks per class for classification.
- Comparisons include 2D-FCN, 3D-UNet, AE-Seg, 3D-Seg, BEAS, FCN, and SR-CNN; metrics include Dice, mean and Hausdorff surface distances, SSIM, mean opinion score, ejection-fraction correlation/bias, and classification accuracy, with Wilcoxon signed-rank tests for top methods.
Limitations
- The paper states that 3D-US training data were limited to 15 image sequences; segmentation results could improve by segmenting ED and ES frames simultaneously or using temporal information such as optical flow; classification accuracy could improve by training the AE and T-L models with a classification objective; and achieving the best possible segmentation accuracy was not the main focus.
- The paper also notes that the T-L regulariser is used only at training time, that latent codes are expected to be correlated because the decoder merges codes across spatial dimensions, and that a variational autoencoder was not used because the K-L term would reduce representation power and generative sampling was not needed.
- Evident from the text: the method is evaluated on cardiac applications only, and the authors state future work will extend ACNN to tasks such as human pose estimation and landmark localisation.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“(II) An implicit statistical parametrisation of the left ventricular shape via NNs for pathology classification;”
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In the paper
“The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks.”
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In the paper
“Our evaluation shows: (I) A sub-pixel cardiac MR image segmentation approach that, in contrast to previous CNN approaches [44], [2], is robust against slice misalignment and coverage problems;”
What the sources say
- Not checked yetcites [44] [44] P. V. Tran. A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 , 2016. - source
- Not matchedcites [2] [2] M. Avendi, A. Kheradvar, and H. Jafarkhani. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. MedIA , 30:108-119, 2016. -
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In the paper
“We show that the proposed approach can be easily adapted to different analysis tasks ( e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The main contribution of ACNN model over the standard FCN approaches is the improved shape delineation of the LV, as it can be seen in terms of the distance error metrics.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
None of the sources cited for this claim have been checked yet.
In the paper
“Slice by slice analysis [2], [44] significantly under-performs compared to the proposed subpixel and ACNN-Seg segmentation methods.”
What the sources say
- Not matchedcites [2] [2] M. Avendi, A. Kheradvar, and H. Jafarkhani. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. MedIA , 30:108-119, 2016. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [44] [44] P. V. Tran. A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 , 2016. - source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, the dice score metrics are observed to be lower since 2D analysis can yield poor performance in basal and apical parts of the heart as shown in Fig. 6.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
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In the paper
“Instead of applying these two gradient descent updates sequentially in an iterative fashion, we perform a joint update training scheme and experimentally observed better convergence.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The experimental results, given in Table II, show that neural network models outperforms previous state-of-theart approaches in this public benchmark dataset although the training data size was limited to 15 image sequences.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
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In the paper
“As can be seen in the bottom row of Fig. 6, without the learnt shape priors, label map predictions are more prone to imaging artefacts.”
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- It may state two separate claims as one. 34
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In the paper
“We show that the proposed segmentation and SR models become more robust against imaging artefacts mentioned earlier which is underlined by our state-of-the-art results on the MICCAI'14 CETUS public benchmark [8].”
What the sources say
- Not checked yetcites [8] [8] O. Bernard, J. G. Bosch, B. Heyde, M. Alessandrini, D. Barbosa, S. Camarasu-Pop, F. Cervenansky, S. Valette, O. Mirea, M. Bernier, et al. Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography. IEEE TMI , 35(4):967-977, 2016. - source
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In the paper
“(III) An image SR technique that extends previous work [34] and that is robust against slice misalignments;”
What the sources say
- Not checked yetcites [34] [34] O. Oktay, W. Bai, M. Lee, R. Guerrero, K. Kamnitsas, J. Caballero, A. de Marvao, S. Cook, D. ORegan, and D. Rueckert. Multi-input cardiac image super-resolution using convolutional neural networks. In International Conference on MICCAI , pages 246-254. Springer, 2016. - source
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In the paper
“our approach is computationally more efficient than the state-of-the-art SR-CNN model [34] as the feature extraction is performed in the low-dimensional image space.”
What the sources say
- Not checked yetcites [34] [34] O. Oktay, W. Bai, M. Lee, R. Guerrero, K. Kamnitsas, J. Caballero, A. de Marvao, S. Cook, D. ORegan, and D. Rueckert. Multi-input cardiac image super-resolution using convolutional neural networks. In International Conference on MICCAI , pages 246-254. Springer, 2016. - source
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ACNN-Seg | 1.37 ± 0.42 | 7.89 ± 3.83 | .939 ± .017 | 1.14 ± 0.22 | 7.31 ± 3.59 | .811 ± .027 | 1 . 60 × 10 6 |”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: The high-resolution motion-free cardiac MR images provide an appropriate reference for evaluating segmentation performance on low-resolution 2D stacks.
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In the paper
“It is observed that the learnt latent representations in general follow a normal distribution and they are not separated in multi-clusters ( e.g. mixture of Gaussians).”
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In the paper
“In particular, Hausdorff distances were reduced significantly”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
- Rests on an assumption: The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
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In the paper
“Previous slice by slice segmentation approaches validated their methods on LR annotations; however, we see that the produced label maps are far off from the true underlying ventricular geometry and it can be a limiting factor for the analysis of ventricle morphology.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, we observed an improvement in terms of normalised Dice score, which was quantitatively not significant due to large volumetric size of the LV cavity.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is observed that the ACNN approach ensures better consistency between frames although none of the methods have used temporal information.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
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In the paper
“We also analysed the performance change in AE-Seg with respect to the number of parameters, which shows that the small capacity AE-Seg model ( 8 × 10 4 pars) is not suitable for cardiac image segmentation as the second stage in the cascaded model does not improve the performance significantly.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observed that reconstruction of label-maps from low dimensional representations was limited since the ventricle boundaries were not delineated properly but rather a rough segmentation was generated (DSC: . 734 ).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the segmentation problem, the Sorensen-Dice loss was tested in the experiments as an alternative to the cross-entropy loss, yet we observed a degraded performance since the latter is a smoother function.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“By including the codes from ES phase, the classification accuracies were improved to 86 . 6% vs 91 . 6% .”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We used 10 -fold cross-validation on 60 CMR sequences and obtained 76 . 6% vs 83 . 3% accuracy using PCA and T-L codes extracted from ED phase.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“It is also observed to be more memory efficient compared to the standard 3D-UNet architecture [12].”
What the sources say
- Not matchedcites [11] [11] H. Chen, Y. Zheng, J.-H. Park, P.-A. Heng, and S. K. Zhou. Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. In International Conference on MICCAI , pages 487-495. Springer, 2016. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [12] [12] ¨ O. C ¸ ic ¸ek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In Proceedings of MICCAI , pages 424-432. Springer, 2016. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“The proposed global training objective in SR corresponds to a prior on the space of feasible high-resolution (HR) solutions, which is experimentally shown to be useful since SR is an illposed problem.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In that regard, the proposed method is not only useful for image enhancement and segmentation but also for the study of anatomical shape variations in population studies and their associations with cardiac related pathologies.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Our framework learns a non-linear compact representation of the underlying anatomy through a stacked convolutional autoencoder [31] and enforces network predictions to follow the learnt statistical shape/label distributions.”
What the sources say
- Not checked yetcites [31] [31] J. Masci, U. Meier, D. Cires ¸an, and J. Schmidhuber. Stacked convolutional auto-encoders for hierarchical feature extraction. In International - Conference on Artificial Neural Networks , pages 52-59. Springer, 2011. -
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More importantly, our approach is independent of the particular NN architecture or application; it can be combined with any of the state-of-the-art segmentation or super-resolution (SR) NN models and potentially improve its prediction accuracy and robustness without introducing any memory or computational complexity at inference time.”
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In the paper
“We also demonstrate that the lower dimensional representations learnt by the proposed ACNN can be useful for classification of pathologies such as dilated and hypertrophic cardiomyopathy, and it does not require point-wise correspondence search between subjects as in [39].”
What the sources say
- Not matchedcites [39] [39] M. Shakeri, H. Lombaert, S. Tripathi, S. Kadoury, A. D. N. Initiative, et al. Deep spectral-based shape features for Alzheimers disease classification. In SASHIMI , pages 15-24. Springer, 2016. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Similarly, 3D-US is recommended over the use of 2D-US since it has been demonstrated to provide more accurate and reproducible volumetric measurements [25].”
What the sources say
- Not checked yetcites [25] [25] R. M. Lang, L. P. Badano, W. Tsang, D. H. Adams, E. Agricola, T. Buck, F. F. Faletra, A. Franke, J. Hung, L. P. de Isla, et al. EAE/ASE recommendations for image acquisition and display using three-dimensional echocardiography. Journal of the American Society of Echocardiography , 25(1):3-46, 2012. -
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More importantly, our approach is independent of the particular NN architecture or application; it can be combined with any of the state-of-the-art segmentation or super-resolution (SR) NN models and potentially improve its prediction accuracy and robustness without introducing any memory or computational complexity at inference time.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
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In the paper
“Using the proposed approach we can perform full 3D segmentation without explicit motion correction and do not have to rely on LR slice-by-slice 2D segmentation.”
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In the paper
“In [1], Alain and Bengio showed that the denoising reconstruction error is equivalent to contractive penalty, which forces the feature extraction (encoder) function f resist perturbations of the input and contracts these input samples to similar low dimensional codes.”
What the sources say
- Not checked yetcites [1] [1] G. Alain and Y. Bengio. What regularized auto-encoders learn from the data-generating distribution. Journal of Machine Learning Research , 15(1):3563-3593, 2014. -
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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In the paper
“As for the case of image segmentation, learnt shape representations can be used to regularise image SR, constraining the model to make only anatomically meaningful predictions.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“It provides two main advantages over the state-of-the-art medical image SR model proposed in [34]: (I) the network generates image features in the LR image grid rather than early upsampling of the features, which reduces memory and computation requirements significantly.”
What the sources say
- Not checked yetcites [34] [34] O. Oktay, W. Bai, M. Lee, R. Guerrero, K. Kamnitsas, J. Caballero, A. de Marvao, S. Cook, D. ORegan, and D. Rueckert. Multi-input cardiac image super-resolution using convolutional neural networks. In International Conference on MICCAI , pages 246-254. Springer, 2016. - source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The given penalty function promotes the network to learn the underlying low-dimensional data manifold and capture its local smooth structure.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The experimental results were evaluated in a blinded fashion by uploading the generated segmentations from separate 30 sequences into the CETUS web platform.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Low dimensional encoding enables us to train models with global characteristics but also yields better generalisation power for the underlying anatomy as shown in earlier work [43].”
What the sources say
- Not checked yetcites [43] [43] A. Torralba and Y. Weiss. Small codes and large image databases for recognition. In Proceedings of CVPR , pages 1-8. IEEE, 2008. - source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to the smoothness of the latent distributions, the extracted codes are expected to be correlated since the decoder merges some of the codes along the three spatial dimensions to construct input feature maps for the transposed convolutions, but this characteristic is not a limitation in our study.”
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Image SR is an ill-posed problem as there are an infinite number of solutions for a given input sample but only a few would be anatomically meaningful and accurate.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to imposing shape regularisation, this parametrisation encourages label consistency in model predictions, and reduces false-positive detections as they can influence the predicted codes in the hidden layer.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“global regularisation reduces the amount of spurious false positive predictions and enforces abnormal LV segmentation shapes to fit into the learnt representation model.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is important to note that the T-L regulariser model is used only at training time but not during inference; in other words, the fully convolutional (FCN) segmentation and super-resolution models can still be used for applications using different image sizes.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, we believe that adversarial training comes at the expense of less interpretability of the regularisation term and unstable model training behaviour, which still remains an open research problem.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“In SR application, AL enables the SR network to hallucinate fine texture detail, and the synthesized HR images appear qualitatively more realistic. However, at the same time the PSNR and SSIM scores are usually worse.”
What the sources say
- Not checked yetcites [26] [26] C. Ledig, L. Theis, F. Husz´ ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 , 2016. -
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proposed regulariser model can be seen as an application-specific training objective.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The 3DSeg approach uses the convolution kernels more efficiently without requiring the model to output a high-dimensional intensity image.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The clinical HR images used as reference for SR evaluation are accurate ground truth.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The presented ACNN framework is not only limited to the medical image segmentation and SR tasks but can be extended to other image analysis tasks where prior knowledge can provide model guidance and robustness.”
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Such approaches have been used successfully in natural image super-resolution (SR) [26] and segmentation [30] tasks.”
What the sources say
- Not checked yetcites [26] [26] C. Ledig, L. Theis, F. Husz´ ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 , 2016. - source
- Not checked yetcites [30] [30] P. Luc, C. Couprie, S. Chintala, and J. Verbeek. Semantic segmentation using adversarial networks. In NIPS Workshop on Adversarial Training , 2016. - source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For this reason, the authors of [26] have pointed out that adversarial training may not be suitable for medical applications, where the accuracy and fidelity of the visual content more important than the qualitative appearance of the HR images.”
What the sources say
- Not checked yetcites [26] [26] C. Ledig, L. Theis, F. Husz´ ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 , 2016. -
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the additional K-L divergence term (constraint) would reduce the representation power of the AE; thus, the local anatomical variations would not be captured in detail.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, non-linear layers are not applied on the lower dimensional latent representations since that would further constrain the autoencoder.”
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“More importantly, cascaded approaches add additional computational complexity due to the increased number of filters, which could be redundant given that the standard segmentation model is able to capture shape properties of the organs as long as it has a large receptive field and is optimised with shape constraints.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast to this, our AE model is trained solely on cardiac segmentation masks and features are customised to identify anatomical variations observed in the heart chambers.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
The high-resolution motion-free cardiac MR images provide an appropriate reference for evaluating segmentation performance on low-resolution 2D stacks.
“Since the ground-truth information is obtained from the HR motionfree images, the experimental results are expected to reflect the performance of the method with respect to an appropriate reference.”
The paper relies on this without support.
A perfect mapping between intensity and label spaces is practically not achievable.
“Since a perfect mapping between the intensity and label spaces is practically not achievable, the T-L learnt codes are expected to be more representative due to the inclusion of additional information.”
The paper relies on this without support.
Anatomical objects in medical images are naturally more constrained in shape and location than objects in natural images.
“Inclusion of priors in medical imaging could potentially have much more impact compared to their use in natural image analysis since anatomical objects in medical images are naturally more constrained in terms of their shape and location.”
The authors state this as an assumption.
The proposed ACNN is one of the earliest studies demonstrating the use of convolutional autoencoder networks to learn anatomical shape variations from medical images.
“More importantly, to the best of our knowledge, this is one of the earliest studies demonstrating the use of convolutional autoencoder networks to learn anatomical shape variations from medical images.”
The paper relies on this without support.
The HR ground-truth label maps projected from HR image space are an accurate reference for segmentation evaluation.
“The corresponding groundtruth label maps, however, are projected from the HR image space, which are annotated in the HR image grid.”
The paper relies on this without support.
The clinical HR images used as reference for SR evaluation are accurate ground truth.
“The quality of the upsampled images is evaluated in terms of SSIM metric [47] between the clinical HR image data and reconstructed HR images.”
The paper relies on this without support.
The two expert cardiologists' MOS ratings reliably quantify image quality.
“Two expert cardiologists were asked to rate the upsampled images from 1 (very poor) to 5 (excellent) based on the accuracy of the reconstructed LV boundary and geometry.”
The paper relies on this without support.
The CETUS web platform evaluation is a valid blinded benchmark for segmentation accuracy.
“The experimental results were evaluated in a blinded fashion by uploading the generated segmentations from separate 30 sequences into the CETUS web platform.”
The paper relies on this without support.
The 2D stack LR images are a commonly used acquisition protocol for cardiac imaging.
“only the 2D stack LR images are used, which is a commonly used acquisition protocol for cardiac imaging”
The paper relies on this without support.
The LV blood pool is larger than the myocardium.
“due to the larger size of the LV blood pool compared to the myocardium.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“the authors report performance superior to 3D-ACNN Oktay et al. (2017), achieving 4.3% increase in Dice and 4.15mm (40% less) improvement in Hausdorff distance .”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Oktay et al. (2017)
In the citing paper
“The authors attribute the inferior performance by Ravishankar et al. (2017) to over-regularization, which they have overcome by replacing CDAE with a frozen encoder during training.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Oktay et al. (2017)
In the citing paper
“The suggested model achieves 1.2% and 2.0% improvement respectively in Dice over Ravishankar et al. (2017) for 3D endocardium and myocardium segmentation on a cine-MR dataset (1000 training and 200 testing).”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Oktay et al. (2017)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Cross-entropy and Euclidean gradients have their roles reversed
MinorThe text defines cross-entropy as the segmentation-reconstruction loss and Euclidean distance as the loss matching the predictor's latent codes. It then assigns the predictor-matching role to the first gradient, ∂L x, and the reconstruction role to the second, ∂L h-the reverse of those definitions.
“The first gradient encourages the encoder to generate codes that could be easily extracted by the predictor while the second gradient making sure that a good segmentation-reconstruction can be obtained at the output of the decoder.”
“In the first stage, the AE is trained separately with ground-truth segmentation masks and cross-entropy loss L x . Later, the predictor model is trained to match the learnt latent space h by minimising the Euclidean distance L h between the codes predicted by the AE and predictor as shown in Fig. 3.”
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 4×
“In that regard, our model differentiates from the VGG-Net [42] feature based training objectives [26], [21].”
MethodInfluentialU-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · cited 6×
“In that regard, the proposed SR model and U-Net segmentation models are concatenated to obtain HR segmentation results.”
BackgroundMethodInfluentialImage quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · cited 1×
“The quality of the upsampled images is evaluated in terms of SSIM metric [47] between the clinical HR image data and reconstructed HR images.”
MethodFully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 1×
“In CNN based segmentation models [22], [29], [38], this task is performed by learning a discriminative function that models the underlying conditional probability distribution P(ys |x).”
MethodA survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · cited 2×
“As explained in a recent NN survey paper [28], the majority of the classification and regression models utilise a pixel-”
BackgroundConditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · cited 5×
“ture based training objectives [21], [26].”
BackgroundMethodInfluentialSelf-citationPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z.-H., et al. (2016) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1609.04802 · Cited by 12,624
Self-citationPerceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · cited 3×
“Similar modifications of the objective function during training have been introduced to enhance the quality of natural images, such as perceptual [21] and adversarial [26] loss terms, which were used to synthesise more realistic images in terms of texture and object boundaries.”
Method3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Flagged first, then by citations
7 of the 60 references couldn’t be matched to a known paper.
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Items 201–300 of 758
A Light-Weight Practical Framework for Feces Detection and Trait Recognition
Leng, L., Yang, Z., Kim, C., et al. (2020) · Sensors · doi:10.3390/s20092644 · Cited by 31
Awasthi, N., Vermeer, L., Fixsen, L. S., Lopata, R. G. P., & Pluim, J. P. W. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3169684 · Cited by 32
Prior-aware autoencoders for lung pathology segmentation
Astaraki, M., Smedby, Ö., & Wang, C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102491 · Cited by 31
A review on optimization techniques for medical image analysis
Kaur, P., & Singh, R. K. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7443 · Cited by 33
Machine Learning Approaches for Myocardial Motion and Deformation Analysis
Duchateau, N., King, A. P., & Craene, M. D. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00190 · Cited by 31
Luo, G., Dong, S., Wang, W., Wang, K., Cao, S., Tam, C., Zhang, H., Howey, J., Ohorodnyk, P., & Li, S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101591 · Cited by 30
Artificial Intelligence and Texture Analysis in Cardiac Imaging
Mannil, M., Eberhard, M., von Spiczak, J., Heindel, W. L., Alkadhi, H., & Baeßler, B. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01402-1 · Cited by 31
Smart Health of Ultrasound Telemedicine Based on Deeply Represented Semantic Segmentation
Shen, Y., Zhang, H., Fan, Y., Lee, A. P., & Xu, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3029957 · Cited by 31
Sharan, T. S., Tripathi, S., Sharma, S., & Sharma, N. (2021) · IETE Technical Review · doi:10.1080/02564602.2021.1955760 · Cited by 30
Moal, O., Roger, É., Lamouroux, A., Younes, C., Bonnet, G., Moal, B., & Lafitte, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105637 · Cited by 31
Zhou, Y., Ye, J., Du, Y., et al. (2020) · Sensing and Imaging · doi:10.1007/s11220-020-00283-6 · Cited by 29
Gradual back-projection residual attention network for magnetic resonance image super-resolution
Qiu, D., Cheng, Y., & Wang, X. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106252 · Cited by 29
Left Ventricle Segmentation in Echocardiography with Transformer
Liao, M., Lian, Y., Yao, Y., Chen, L., Gao, F., Xu, L., Huang, X., Feng, X., & Guo, S. (2023) · Diagnostics · doi:10.3390/diagnostics13142365 · Cited by 32
“Oktay et al. [11] built up a model named anatomically constrained neural network (ACNN) for 3D LV segmentation, achieving an average Dice similarity level of 0.912 (ED) and 0.873 (ES) on the CETUS dataset.”
MethodInfluentialWang, Y., Zhang, Y., Wen, Z., Tian, B., Kao, E., Liu, X., Xuan, W., Ordovas, K. G., Saloner, D., & Liu, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-169 · Cited by 30
Li, F., Li, W., Qin, S., & Wang, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106776 · Cited by 29
DeepSSM: A blueprint for image-to-shape deep learning models
Bhalodia, R., Elhabian, S., Adams, J., Tao, W., Kavan, L., & Whitaker, R. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103034 · Cited by 40
“There are other works that incorporate shape priors such as [41] regularizes anatomy segmentation by learning low-dimensional nonlinear shape representations as a constraint.”
BackgroundSfakianakis, C., Simantiris, G., & Tziritas, G. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104557 · Cited by 28
Zhao, D., Ferdian, E., Talou, G. D. M., Quill, G. M., Gilbert, K., Wang, V. Y., Gamage, T. P. B., Pedrosa, J., D’hooge, J., Sutton, T. M., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1016703 · Cited by 32
CAT: Constrained Adversarial Training for Anatomically-Plausible Semi-Supervised Segmentation
Wang, P., Peng, J., Pedersoli, M., Zhou, Y., Zhang, C., & Desrosiers, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3243069 · Cited by 28
“In comparison, existing approaches require a large number of labeled images to learn a shape prior [5], [9], [10] or a complex and problem-specific step involving atlas registration [11], [12].”
BackgroundMethodInfluentialA Full-Image Deep Segmenter for CT Images in Breast Cancer Radiotherapy Treatment
Schreier, J., Attanasi, F., & Laaksonen, H. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00677 · Cited by 28
Automating in vivo cardiac diffusion tensor postprocessing with deep learning–based segmentation
Ferreira, P., Martin, R. R., Scott, A. D., et al. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28294 · Cited by 28
Yang, H., Shan, C., Bouwman, R. A., Kolen, A. F., & de With, P. H. N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101842 · Cited by 27
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Tanno, R., Worrall, D., Kaden, E., Ghosh, A., Grussu, F., Bizzi, A., Sotiropoulos, S. N., Criminisi, A., & Alexander, D. C. (2019) · arXiv · doi:10.48550/arxiv.1907.13418 · Cited by 30
“The original implementation of IQT [38–40] employed a variant of random forests (RFs) to model p ( y | x ) while more recent [7, 44, 51, 70] approaches use variants of convolutional neural networks (CNNs).”
BackgroundMethodTowards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Oda, M., Shimizu, N., Oda, H., Hayashi, Y., Kitasaka, T., Fujiwara, M., Misawa, K., Mori, K., & Roth, H. R. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2293499 · Cited by 35
Deep Learning Methods for Cardiovascular Image
Cao, Y., Liu, Z., Zhang, P., Zheng, Y., Song, Y., & Cui, L. (2019) · Journal of Artificial Intelligence and Systems · doi:10.33969/ais.2019.11006 · Cited by 27
An, S., Zhu, H., Wang, Y., Zhou, F., Zhou, X., Xu, Y., Zhang, Y., Liu, X., Jiao, Z., & He, Y. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101983 · Cited by 28
Zareiamand, H., Amin, D., Mohammadi, I., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13162667 · Cited by 27
“[32] devised an auto-encoder-based anatomically restricted neural network (NN) that utilizes constraints to make inferences about limitations.”
MethodA Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging
Yang, F., Zhang, Y., Lei, P., Wang, L., Miao, Y., Xie, H., & Zeng, Z. (2019) · BioMed Research International · doi:10.1155/2019/5636423 · Cited by 25
Integrating 3D Geometry of Organ for Improving Medical Image Segmentation
Yao, J., Cai, J., Yang, D., Xu, D., & Huang, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32254-0_36 · Cited by 24
DSANet: Dual-Branch Shape-Aware Network for Echocardiography Segmentation in Apical Views
Zhou, G., Zhang, W., Shi, Z.-Q., Qi, Z., Wang, K., Song, H., Yao, J., & Chen, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3293520 · Cited by 27
“Similarly, prior information, such as an atlas, has also been exploited in deep-learning models to improve the segmentation with anatomical structure knowledge [15].”
BackgroundMulti-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography
Ta, K., Ahn, S. S., Thorn, S., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3355383 · Cited by 27
“Oktay et al. [36] proposed a more end-to-end 3D segmentation method that utilized autoencoder generated anatomical shape priors to constrain 3D volumetric predictions of the LV myocardium.”
MethodTang, X., Peng, J., Zhong, B., Li, J., & Yan, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106110 · Cited by 25
Yang, Z., Soltanian-Zadeh, S., Chu, K. K., et al. (2021) · Biomedical Optics Express · doi:10.1364/boe.434775 · Cited by 26
Dual-Branch TransV-Net for 3-D Echocardiography Segmentation
Zhang, J., Wang, Y., Chen, L., et al. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3249904 · Cited by 25
Zhang, S., Wu, S., Shang, S., et al. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2939810 · Cited by 25
“Parameter J [47] indicated the similarity between two finite datasets.”
BackgroundMethodHerz, C., Pace, D. F., Nam, H. H., Lassó, A., Dinh, P. V., Flynn, M., Cianciulli, A. R., Golland, P., & Jolley, M. A. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.735587 · Cited by 25
“proposed a framework for DL-based segmentation of the left ventricle (LV) in 3DE images using shape priors to follow global anatomical properties (23).”
MethodBrahim, K., Arega, T. W., Boucher, A., et al. (2022) · Sensors · doi:10.3390/s22062084 · Cited by 25
“[31] modified the decoder layers of a U-Net architecture to embed prior information through super-resolution gold standard maps using cardiac cine MRI.”
MethodZhao, Z., Hu, J., Zeng, Z., Yang, X., Qian, P., Veeravalli, B., & Guan, C. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897591 · Cited by 30
“ing methods on automatic cardiac image segmentation [4], massive densely pixel-wise annotations are demanded for training, which are complicated to acquire in clinical practice since the annotation process is expensive, laborious, and time-consuming, leading to label scarcity.”
Fazekas, B., Lachinov, D., Aresta, G., Mai, J., Schmidt-Erfurth, U. M., & Bogunović, H. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3217962 · Cited by 30
“, shadowing, eye-movement or low-resolution acquisition [15].”
BackgroundAsha, S. B., Gopakumar, G., & Gorthi, R. K. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105704 · Cited by 22
Ta, K., Ahn, S. S., Lu, A., Stendahl, J. C., Sinusas, A. J., & Duncan, J. S. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098664 · Cited by 28
“We minimize the gradient of the displacement field, which would be maximized if divergence is present, as follows: Ldiv = 1 N N∑ i=2 ∇Ui (3) LV shape is enforced by incorporating anatomical priors as proposed by Otkay et al [12].”
BackgroundMethodInfluentialResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images
Amer, A., Ye, X., Zolgharni, M., et al. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9175436 · Cited by 25
“[7], used convolution neural network to propose an approach named anatomically constrained neural network (ACNN), which uses auto-encoder to fit non-linear compact representation of the left ventricle structure, achieving a dice similarity measure of 0.”
BackgroundMethodAcero, J. C., Zacur, E., Xu, H., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21949-9_39 · Cited by 24
Xue, W., Cao, H., Ma, J., Bai, T., Wang, T., & Ni, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3221429 · Cited by 26
“The competitors include both classical ones that based on structured random forest algorithm(SRF) [5] and B-Spline explicit active surface model in fully automatic model (BEASM-fully) and semi-automatic mode (BEASM-semi) [12], and deep learning-based ones based on U-Net [38], anatomical prior knowledge (ACNN) [41], st…”
MethodDeep Learning in Single-cell Analysis
Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 38
“One of the most popular CNNs, U-Net [305], has shown significant success in many medical image segmentation tasks [306, 307, 308, 309, 310, 311, 312, 313].”
BackgroundLéger, J., Brion, E., Desbordes, P., et al. (2020) · Applied Sciences · doi:10.3390/app10031154 · Cited by 24
Contour Transformer Network for One-Shot Segmentation of Anatomical Structures
Lu, Y., Zheng, K., Li, W., Wang, Y., Harrison, A. P., Lin, C., Wang, S., Xiao, J. Z., Lü, L., Kuo, C., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3043375 · Cited by 24
“[41] employs a shape regularization autoencoder in a segmentation network to”
MethodCoordConv-Unet: Investigating CoordConv for Organ Segmentation
Jurdi, R. E., Petitjean, C., Honeine, P., & Abdallah, F. (2021) · IRBM · doi:10.1016/j.irbm.2021.03.002 · Cited by 22
Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions
Parashar, G., Chaudhary, A., & Rana, A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00848-6 · Cited by 23
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
Wang, S., Qin, C., Savioli, N., Chen, C., O’Regan, D. P., Cook, S. A., Guo, Y., Rueckert, D., & Bai, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_2 · Cited by 25
“Besides images, machine learning algorithms have also been proposed for enhancing the segmentations [14,7,9].”
MethodSelf-citationBoutillon, A., Conze, P.-H., Pons, C., Burdin, V., & Borotikar, B. S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102556 · Cited by 25
“…anatomical representation is then integrated in the segmentation network during optimization, by enforcing the predicted segmentation to be close to the ground truth in anatomical space using a regularization term based on Euclidean distance (Ravishankar et al., 2017; Oktay et al., 2018a).”
BackgroundMethodInfluentialTsamos, A., Evsevleev, S., Fioresi, R., et al. (2023) · Journal of Imaging · doi:10.3390/jimaging9020022 · Cited by 23
Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
Ji, X. H., Guo, S., Zhang, H., et al. (2023) · Applied Sciences · doi:10.3390/app13179535 · Cited by 23
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Kuzina, A., Egorov, E., & Burnaev, E. V. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00844 · Cited by 22
Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications
Jackson, G. P., Hu, J., & Health, S. E. F. T. I. Y. S. O. A. I. I. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677925 · Cited by 22
“Based on ratings and comments from peer review, section editors’ recommendations, and input from the IMIA Yearbook Editorial Board, three papers [3, 8, 11] were chosen as 2018 best papers in the AI for Health special section of the IMIA Yearbook.”
BackgroundBiomedical imaging and analysis through deep learning
Drukker, K., Yan, P., Sibley, A., & Wang, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00004-1 · Cited by 23
An adaptive enhancement algorithm based on visual saliency for low illumination images
Qian, S., Shi, Y., Wu, H., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02466-4 · Cited by 22
Tackling the class imbalance problem of deep learning-based head and neck organ segmentation
Tappeiner, E., Welk, M., & Schubert, R. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02649-5 · Cited by 22
Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity
Du, H., Dong, Q., Xu, Y. L., & Liao, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3269523 · Cited by 22
“Researchers [31], [32], [33], [34], [35], [36] mostly minimize the distance between the segmentation network output and the pre-defined anatomical priors.”
BackgroundCloud Deployment of High-Resolution Medical Image Analysis With TOMAAT
Milletarì, F., Frei, J., Aboulatta, M., Vivar, G., & Ahmadi, S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2885214 · Cited by 23
“This includes a multi-view 2D FCN (MV-FCN) [11], a FCNN architecture with anatomical shape regularization (ACNN) [12], and a B-spline Explicit Active Surfaces (BEAS)method [13].”
MethodByrne, N., Clough, J. R., Montana, G., & King, A. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68107-4_1 · Cited by 29
Karhade, J., Ghosh, S. K., Gajbhiye, P., Tripathy, R. K., & Acharya, U. R. (2021) · Applied Sciences · doi:10.3390/app11177920 · Cited by 21
Shape-Guided Dual Consistency Semi-Supervised Learning Framework for 3-D Medical Image Segmentation
Lei, T., Liu, H., Wan, Y., Li, C., Xia, Y., & Nandi, A. K. (2023) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2023.3286866 · Cited by 22
“the segmentation network to follow the learned statistical shape distribution [66].”
BackgroundAttention Based Cross-Domain Synthesis and Segmentation From Unpaired Medical Images
Liu, X., Pan, J., Li, X., Wei, X., Liu, Z., Pan, Z., & Tang, J. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3296499 · Cited by 21
MRI Whole Heart Segmentation Using Discrete Nonlinear Registration and Fast Non-local Fusion
Heinrich, M. P., & Oster, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_25 · Cited by 21
Self-citationSmistad, E., Østvik, A., Salte, I. M., Leclerc, S., Bernard, O., & Løvstakken, L. (2018) · IEEE International Ultrasonics Symposium · doi:10.1109/ultsym.2018.8579886 · Cited by 21
“This has been an active research area for several decades for both 2D and 3D ultrasound, and recently it has been shown that deep neural networks can also perform this efficiently and accurately [3–6].”
Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints
Li, R., Chen, H., Gong, G., & Wang, L. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176112 · Cited by 23
“In ACNN [12], a stacked convolutional AE was used to reconstruct the label images which mainly contains shape prior knowledge and generate a 64-dimensional parametrization to represent the global information.”
BackgroundMethodInfluentialAutomatic teeth segmentation on panoramic X-rays using deep neural networks
Nader, R., Smorodin, A., Fourniere, N. D. L., Amouriq, Y., & Autrusseau, F. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956708 · Cited by 21
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
Cherukuri, V. (n.d.) · ScholarSphere (Penn State Libraries) · Cited by 21
An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image
Liu, Y., Wang, W., Wang, K., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39074-7_23 · Cited by 20
“e (LV). They firstly initialized the LV by a two-step registration method and then adopted principal components to estimate the LV. At last, the myocardium was refined on the poly space. Oktay et al. [4] incorporated global shape information into CNN. They utilized autoencoder to estimate the global shape information…”
MethodCNN in CT Image Segmentation: Beyond Loss Function for Exploiting Ground Truth Images
Song, Y., Yu, Z. T., Zhou, T., et al. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098488 · Cited by 20
“(1) Regularization-based methods [3, 4, 5] focus on devising the loss function.”
MethodUncertain-DeepSSM: From Images to Probabilistic Shape Models
Adams, J., Bhalodia, R., & Elhabian, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61056-2_5 · Cited by 20
“asound images. Milletari et al. [38] segment ultrasound images using low-dimensional shape representation in the form of principal component analysis (PCA) to regress landmark positions. Oktay et al. [41] incorporate prior knowledge about organ shape and location into a deep network to anatomically constrain resulting…”
MethodArtificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation
Kwan, A. C., Salto, G., Cheng, S. C., & Ouyang, D. (2021) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-021-00678-4 · Cited by 21
The Role of AI in Characterizing the DCM Phenotype
Asher, C., Anton, E. P., Rizvi, M., Ruijsink, B., Chiribiri, A., Razavi, R., & Carr‐White, G. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.787614 · Cited by 21
Boutillon, A., Borotikar, B. S., Burdin, V., & Conze, P.-H. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102364 · Cited by 20
Exploiting Vector Attention and Context Prior for Ultrasound Image Segmentation
Xu, L., Gao, S., Shi, L., Wei, B., Liu, X., Zhang, J., & He, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.033 · Cited by 21
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Gao, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01064 · Cited by 40
“For example, integrating graphical models to embed spatial and anatomical knowledge [11, 35], or introducing a shape prior knowledge regularization loss through an autoencoder [20, 48].”
MethodWu, L., Hu, S., & Liu, C. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5577956 · Cited by 19
“However, in most recently developed medical image analysis techniques, it is not obvious how to incorporate such prior knowledge [59].”
BackgroundFatima, N., Afrakhteh, S., Iacca, G., & Demi, L. (2024) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2024.3393026 · Cited by 21
Boutillon, A., Borotikar, B. S., Burdin, V., et al. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098360 · Cited by 19
“3: Automatic pathological segmentation of scapula using UNet [1], CAE-UNet [5], cGAN-UNet [8] and proposed method.”
MethodInfluentialAn Alarm System for Segmentation Algorithm Based on Shape Model
Liu, F., Xia, Y., Yang, D., Yuille, A., & Xu, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01075 · Cited by 25
Saranya, A., Kottursamy, K., AlZubi, A. A., & Bashir, A. K. (2021) · Soft Computing · doi:10.1007/s00500-021-06519-1 · Cited by 19
RetractedGenerative adversarial networks and adversarial methods in biomedical image analysisPreprint
Wolterink, J. M., Kamnitsas, K., Ledig, C., & Išgum, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10352 · Cited by 19
“ces of dierent manufacturers and settings. Adversarial UDA oered signicant improvements for the segmentation of the left atrium, which were also complementary to benets obtained from shape priors [56]. Besides segmentation tasks, Lafarge et al. [57] investigated the approach 19 Figure 11: Result from Kamnitsas et al […”
MethodSelf-citationZhuang, Z., Jin, P., Raj, A. N. J., et al. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/3772129 · Cited by 18
“[15] further extended the U-Net model to improve the accuracy of LV segmentation.”
MethodUrsuleanu, T. F., Luca, A. R., Gheorghe, L., Grigorovici, R., Iancu, S., Hlusneac, M., Preda, C., & Grigorovici, A. (2021) · Diagnostics · doi:10.3390/diagnostics11081373 · Cited by 20
Chen, Q., Pan, G., Zhao, L., Fan, J., Chen, W., & Zhang, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3227245 · Cited by 18
“This is particularly notable in real-world applications where background environments are not ideal, such as in extreme weather like snow, storm, ice, and strong light [8], [9], 10].”
BackgroundOptimal Fuzzy Intensification System for Contrast Distorted Medical Images
Subramani, B., Veluchamy, M., & Bhandari, A. K. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3320971 · Cited by 18
Shape-intensity-guided U-net for medical image segmentation
Dong, W., Du, B., & Xu, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128534 · Cited by 19
Super Resolution of Cardiac Cine MRI Sequences Using Deep Learning
Basty, N., & Grau, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00946-5_3 · Cited by 18
Speckle reduction of medical ultrasound images using deep learning with fully convolutional network
Kazuma, A., Nagaoka, R., & Hasegawa, H. (2020) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ab80a5 · Cited by 18
Fu, H., Sun, G., Zabalza, J., Zhang, A., Ren, J., & Jia, X. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2992230 · Cited by 18
“Recently, deep learning has become a hot research topic in image processing and is utilized for various applications, such as change detection [25], image classification [26], [27], and image segmentation [28].”
Dong, Z., Du, X., & Liu, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106210 · Cited by 18
A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging
Edwards, K., Khoshdel, V., Asefi, M., et al. (2021) · Electronics · doi:10.3390/electronics10060674 · Cited by 17
Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks
Zhou, S., Nie, D., Adeli, E., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_56 · Cited by 17
Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Balsiger, F., Soom, Y., Scheidegger, O., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_31 · Cited by 17
Linearly Constrained Neural NetworksPreprint
Hendriks, J., Jidling, C., Wills, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.01600 · Cited by 17
Retracted: Analysis of Lung Cancer Detection Based on the Machine Learning Algorithm and IOT
Karthick, K., Rajkumar, S., Selvanathan, N., et al. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489084 · Cited by 16
“The essential issue in image handling is image quality overhauling by the commotion decrease [13].”
BackgroundRetractedDeep Neural Network for Cardiac Magnetic Resonance Image Segmentation
Chen, D. J., Bhopalwala, H. M., Dewaswala, N., Arunachalam, S. P., Enayati, M., Farahani, N. Z., Pasupathy, K. S., Lokineni, S., Bos, J. M., Noseworthy, P. A., et al. (2022) · Journal of Imaging · doi:10.3390/jimaging8050149 · Cited by 19
Learning continuous shape priors from sparse data with neural implicit functions
Amiranashvili, T., Lüdke, D., Li, H., Stefan Zachow, A., & Menze, B. H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103099 · Cited by 16
Zeng, G., Wang, Q., Lerch, T. D., Schmaranzer, F., Tannast, M., Siebenrock, K. A., & Guoyan Zheng, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00919-9_22 · Cited by 16
Metadata
- Authors
- Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., Kainz, B., Glocker, B., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-02
- Volume
- 37
- Issue
- 2
- Pages
- 384-395
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2620296437
- Semantic Scholar
- 33f7b95903eeefdde37fabf9655b4de089d53bd2
- Cited by
- 751
- References
- 60
- Citations by year
- 2026: 34 · 2025: 64 · 2024: 61 · 2023: 87 · 2022: 81 · 2021: 135 · 2020: 118 · 2019: 119 · 2018: 47
- FWCI
- 32.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- National University of Singapore (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- EPSRC Program (EP/P001009/1)
- British Heart Foundation, U.K. (PG/12/27/29489)
- National Institute for Health Research (NIHR) Biomedical Research Centre-based at Imperial College Healthcare NHS Trust
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- anatomical prior knowledge, cardiac image segmentation, biomarkers, artifact reduction
- MeSH terms
- Algorithms, Heart, Humans, Magnetic Resonance Imaging, Cardiomyopathies, Databases, Factual, Neural Networks, Computer, Imaging, Three-Dimensional, Cardiac Imaging Techniques
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