Deep neural networks for anatomical brain segmentation
Abstract
We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture a local spatial context while downscaled large 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.
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De Brebisson et al. achieved competitive results (DSC 72.5±16.3) in MICCAI 2012 challenge as first CNN approach.
“They achieved competitive results (DSC = 72.5% ∓ 16.3%) in MICCAI 2012 challenge on multi-atlas labeling as the first CNN approach applied to the task.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [40]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
(2013) · Cited by 3,740 · see citing works
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163 · see citing works
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536 · see citing works
(2012) · neural information processing systems · Cited by 1,291 · see citing works
The Tradeoffs of Large-Scale Learning
Bottou, L., & Bousquet, O. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.003.0015 · Cited by 1,201 · see citing works
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
Ranzato, M., Huang, F., Boureau, Y.-L., & LeCun, Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383157 · Cited by 1,186 · see citing works
Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.05.061 · Cited by 865 · see citing works
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
Pierrick Coupé, A., Manjón, J. V., Fonov, V., Pruessner, J. C., Robles, M., & Collins, D. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.018 · Cited by 684 · see citing works
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_31 · Cited by 582 · see citing works
Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future
Petrella, J. R., Coleman, R. E., & Doraiswamy, P. M. (2003) · Radiology · doi:10.1148/radiol.2262011600 · Cited by 427 · see citing works
Toward automatic phenotyping of developing embryos from videos
Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., & Barbano, P. E. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852470 · Cited by 260 · see citing works
Structural changes of the substantia nigra in Parkinson's disease as revealed by MR imaging.
Hutchinson, M., & Raff, U. (2000) · PubMed · Cited by 116 · see citing works
Mindboggle: Automated brain labeling with multiple atlases
Klein, A., Mensh, B. D., Ghosh, S., Tourville, J. A., & Joy Hirsch, C. (2005) · BMC Medical Imaging · doi:10.1186/1471-2342-5-7 · Cited by 113 · see citing works
Middleton, I., & Damper, R. I. (2003) · Medical Engineering & Physics · doi:10.1016/s1350-4533(03)00137-1 · Cited by 96 · see citing works
Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network
Cernăzanu-Glăvan, C., & Holban, Ş. (2013) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2013.01015 · Cited by 86 · see citing works
Towards a deep learning approach to brain parcellation
Lee, N., Laine, A. F., & Klein, A. (2011) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2011.5872414 · Cited by 23 · see citing works
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A 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 · see citing works
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · see citing works
“In contrast to the 2D/3D hybrid variants (Roth et al. (2014); Brebisson and Montana (2015)), 3D CNNs can fully exploit dense-inference (LeCun et al. (1998); Ser-manet et al. (2014)), a technique that greatly decreases inference times and which we will further discuss in section 2.1.”
BackgroundInfluentialDeep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129 · see citing works
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861 · see citing works
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813 · see citing works
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.041 · Cited by 767 · see citing works
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B. R., & Reuter, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117012 · Cited by 672 · see citing works
Deep learning for magnification independent breast cancer histopathology image classification
Bayramoğlu, N., Kannala, J., & Heikkilä, J. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900002 · Cited by 468 · see citing works
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., & Lladó, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.08.008 · Cited by 440 · see citing works
Landmark-based deep multi-instance learning for brain disease diagnosis
Liu, M., Zhang, J., Adeli, E., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.10.005 · Cited by 424 · see citing works
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
Dolz, J., Desrosiers, C., & Ayed, I. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.039 · Cited by 411 · see citing works
Deep learning based brain tumor segmentation: a survey
Liu, Z., Tong, L., Chen, L., Jiang, Z., Zhou, F., Zhang, Q., Zhang, X., Jin, Y., & Zhou, H. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00815-5 · Cited by 439 · see citing works
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
Milletarì, F., Ahmadi, S., Kroll, C., Plate, A., Rozanski, V. E., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B. B., Bötzel, K., et al. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.04.002 · Cited by 394 · see citing works
Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities
Moeskops, P., Wolterink, J. M., van der Velden, B. H. M., Gilhuijs, K. G. A., Leiner, T., Viergever, M. A., & Išgum, I. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_55 · Cited by 354 · see citing works
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.028 · Cited by 348 · see citing works
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
Wachinger, C., Reuter, M., & Klein, T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.035 · Cited by 352 · see citing works
DRINet for Medical Image Segmentation
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2835303 · Cited by 344 · see citing works
DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRIPreprint
Sarraf, S., DeSouza, D. D., Anderson, J. A. E., Tofighi, G., & Initiative, A. D. N. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/070441 · Cited by 331 · see citing works
Big data analysis for brain tumor detection: Deep convolutional neural networks
Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.065 · Cited by 327 · see citing works
Qureshi, I., Yan, J., Abbas, Q., Shaheed, K., Riaz, A. B., Wahid, A., Khan, M. W. J., & Szczuko, P. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.09.031 · Cited by 302 · see citing works
Salehi, S. S. M., Deniz Erdogmus, B., & Gholipour, A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2721362 · Cited by 232 · see citing works
Variability and reproducibility in deep learning for medical image segmentation
Renard, F., Guedria, S., Palma, N. D., & Vuillerme, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-69920-0 · Cited by 231 · see citing works
Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2020) · Sensors · doi:10.3390/s20113243 · Cited by 225 · see citing works
Zreik, M., Leßmann, N., van Hamersvelt, R. W., Wolterink, J. M., Voskuil, M., Viergever, M. A., Leiner, T., & Išgum, I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.11.008 · Cited by 216 · see citing works
Radiological images and machine learning: Trends, perspectives, and prospects
Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 236 · see citing works
AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation
Coupé, P., Mansencal, B., Clément, M., Giraud, R., Denis de Senneville, B., Ta, V.-T., Lepetit, V., & Manjon, J. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117026 · Cited by 206 · see citing works
Classification of dental diseases using CNN and transfer learning
Prajapati, S. A., Nagaraj, R., & Mitra, S. K. (2017) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi.2017.8053547 · Cited by 192 · see citing works
Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks
Rasti, R., Teshnehlab, M., & Phung, S. L. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.004 · Cited by 184 · see citing works
Zhou, X., Takayama, R., Wang, S., Hara, T., & Fujita, H. (2017) · Medical Physics · doi:10.1002/mp.12480 · Cited by 174 · see citing works
Jyothi, P., & Singh, A. R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10245-x · Cited by 164 · see citing works
Bzdok, D. (2017) · HAL · Cited by 160 · see citing works
Zhou, Z., He, Z., & Jia, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.097 · Cited by 151 · see citing works
Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., & Garnavi, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_29 · Cited by 145 · see citing works
Minnema, J., van Eijnatten, M. A. J. M., Kouw, W. M., Diblen, F., Mendrik, A. M., & Wolff, J. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.10.012 · Cited by 142 · see citing works
Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images
Kang, J., & Gwak, J. (2019) · IEEE Access · doi:10.1109/access.2019.2900672 · Cited by 132 · see citing works
A variant form of 3D-UNet for infant brain segmentation
Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.11.021 · Cited by 133 · see citing works
Kim, G. B., Jung, K.-H., Lee, Y., Kim, H., Kim, N., Jun, S., Seo, J. B., & Lynch, D. A. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0028-9 · Cited by 129 · see citing works
U-Segnet: Fully Convolutional Neural Network Based Automated Brain Tissue Segmentation Tool
Kumar, P., Nagar, P., Arora, C., & Gupta, A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451295 · Cited by 124 · see citing works
CNN-LSTM: Cascaded Framework For Brain Tumour Classification
Shahzadi, I., Tang, T. B., Meriadeau, F., & Quyyum, A. (2018) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2018.8626704 · Cited by 123 · see citing works
Kushibar, K., Valverde, S., González-Villà, S., Bernal, J., Cabezas, M., Oliver, A., & Lladó, X. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.06.006 · Cited by 116 · see citing works
State-of-the-Art Methods for Brain Tissue Segmentation: A Review
Dora, L., Agrawal, S., Panda, R., & Abraham, A. (2017) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2715350 · Cited by 112 · see citing works
Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.03.017 · Cited by 110 · see citing works
Dabiri, S., Popuri, K., Feliciano, E. M. C., Caan, B. J., Baracos, V. E., & Beg, M. F. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.04.007 · Cited by 105 · see citing works
Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net
Zhang, Q., Cui, Z., Niu, X., Geng, S., & Qiao, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70096-0_38 · Cited by 104 · see citing works
Detect-and-segment: A deep learning approach to automate wound image segmentation
Scebba, G., Zhang, J., Catanzaro, S., Mihai, C., Distler, O., Berli, M., & Karlen, W. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100884 · Cited by 103 · see citing works
Zhou, X., Ito, T., Takayama, R., Wang, S., Hara, T., & Fujita, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_12 · Cited by 101 · see citing works
Pinaya, W. H. L., Vieira, S., Garcia‐Dias, R., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00010-9 · Cited by 101 · see citing works
Deep Neural Networks for Fast Segmentation of 3D Medical Images
Fritscher, K. D., Raudaschl, P., Zaffino, P., Spadea, M. F., Sharp, G. C., & Schubert, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_19 · Cited by 97 · see citing works
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Roy, A. G., Conjeti, S., Sheet, D., Katouzian, A., Navab, N., & Wachinger, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_27 · Cited by 95 · see citing works
Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture
Lee, B., Yamanakkanavar, N., & Choi, J. Y. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236493 · Cited by 95 · see citing works
A deep neural network approach for learning intrinsic protein-RNA binding preferences
Ben-Bassat, I., Chor, B., & Orenstein, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty600 · Cited by 89 · see citing works
Computational anatomy for multi-organ analysis in medical imaging: A review
Cerrolaza, J. J., Picazo, M. L., Humbert, L., Sato, Y., Rueckert, D., Ballester, M. Á. G., & Linguraru, M. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.04.002 · Cited by 89 · see citing works
DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs
Singh, R. K., & Gorantla, R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0220677 · Cited by 89 · see citing works
A comprehensive review on brain tumor segmentation and classification of MRI images
Rao, C. S., & Karunakara, K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10443-1 · Cited by 89 · see citing works
MRI tumor segmentation with densely connected 3D CNN
DSouza, A. M., Chen, L., Wu, Y., Abidin, A. Z., Xu, C., & Wismüller, A. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2293394 · Cited by 85 · see citing works
GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation
Huang, Z. R., Zhao, Y., Liu, Y., & Song, G. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102958 · Cited by 84 · see citing works
Mehta, R., Majumdar, A., & Sivaswamy, J. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.2.024003 · Cited by 83 · see citing works
Xue, Y., Farhat, F. G., Boukrina, O. V., Barrett, A. M., Binder, J. R., Roshan, U., & Graves, W. W. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102118 · Cited by 83 · see citing works
Semi-supervised deep learning of brain tissue segmentation
Ito, R., Nakae, K., Hata, J., Okano, H., & Ishii, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.014 · Cited by 80 · see citing works
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
Spitzer, H., Kiwitz, K., Amunts, K., Stefan Harmeling, B., & Dickscheid, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_76 · Cited by 77 · see citing works
Rachmadi, M. F., Hernández, M. V., Agan, M. L. F., Perri, C. D., & Komura, T. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.02.002 · Cited by 78 · see citing works
Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data
Ali, H. M., Kaiser, M. S., & Mahmud, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37078-7_14 · Cited by 76 · see citing works
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Konar, D., Bhattacharyya, S., Panigrahi, B. K., & Behrman, E. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3077188 · Cited by 80 · see citing works
Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks
Ramzan, F., Khan, M. U. G., Iqbal, S., Saba, T., & Rehman, A. U. (2020) · IEEE Access · doi:10.1109/access.2020.2998901 · Cited by 75 · see citing works
Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions
Choi, H. (2017) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-017-0504-7 · Cited by 74 · see citing works
A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI
Sun, L., Ma, W., Ding, X., Huang, Y., Liang, D., & Paisley, J. W. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2937271 · Cited by 70 · see citing works
Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning
Lee, S. M., Kim, H. P., Jeon, K., Lee, S.-H., & Seo, J. K. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab00c9 · Cited by 67 · see citing works
High-resolution 3D abdominal segmentation with random patch network fusion
Tang, Y., Gao, R., Lee, H. H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C. L., Savona, M. R., Abramson, R. G., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101894 · Cited by 67 · see citing works
Contrastive Registration for Unsupervised Medical Image Segmentation
Liu, L., Avilés-Rivero, A. I., & Schönlieb, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3332003 · Cited by 66 · see citing works
Hong, J., Park, B., & Park, H. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037461 · Cited by 64 · see citing works
Wei, J., Xia, Y., & Zhang, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.004 · Cited by 64 · see citing works
A Novel Multimodal MRI Analysis for Alzheimer's Disease Based on Convolutional Neural Network
Wang, Y., Yang, Y., Guo, X., Ye, C., Gao, N., Fang, Y., & Ma, H. T. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512372 · Cited by 63 · see citing works
Sarraf, S., DeSouza, D. D., Anderson, J. A. E., & Saverino, C. (2019) · IEEE Access · doi:10.1109/access.2019.2949577 · Cited by 64 · see citing works
Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis
Jang, K., Russo, C., & Ieva, A. D. (2020) · Neuroradiology · doi:10.1007/s00234-020-02403-1 · Cited by 58 · see citing works
Lazli, L., Boukadoum, M., & Mohamed, O. A. (2020) · Applied Sciences · doi:10.3390/app10051894 · Cited by 57 · see citing works
A review on advances in deep learning
Soniya, S., Paul, S., & Singh, L. (2015) · IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI) · doi:10.1109/wci.2015.7495514 · Cited by 58 · see citing works
Lee, M., Kim, J. Y., Kim, R. E. Y., Kim, H. G., Oh, S. W., Lee, M. K., Wang, S., Kim, N.-Y., Kang, D. W., Rieu, Z., et al. (2020) · Brain Sciences · doi:10.3390/brainsci10120974 · Cited by 56 · see citing works
Evaluation of multislice inputs to convolutional neural networks for medical image segmentation
Vu, M. H., Grimbergen, G., Nyholm, T., & Löfstedt, T. (2020) · Medical Physics · doi:10.1002/mp.14391 · Cited by 57 · see citing works
Deep learning strategies for critical heat flux detection in pool boiling
Rassoulinejad-Mousavi, S. M., Al-Hindawi, F., Soori, T., Rokoni, A., Yoon, H., Hu, H., Wu, T., & Sun, Y. (2021) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2021.116849 · Cited by 56 · see citing works
Shao, M., Han, S., Carass, A., Li, X., Blitz, A. M., Shin, J., Prince, J. L., & Ellingsen, L. M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101871 · Cited by 56 · see citing works
Deep convolutional neural network in medical image processing
Mohapatra, S., Swarnkar, T., & Das, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823014-5.00006-5 · Cited by 53 · see citing works
Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications
Wang, Y., Zu, C., Hu, G., Luo, Y., Ma, Z., He, K., Wu, X., & Zhou, J. (2018) · Neural Processing Letters · doi:10.1007/s11063-017-9759-3 · Cited by 51 · see citing works
Semi-supervised Learning for Segmentation Under Semantic Constraint
Ganaye, P.-A., Sdika, M., & Benoit-Cattin, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_68 · Cited by 51 · see citing works
Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation
Zhu, H., Shi, F., Wang, L., Hung, S., Chen, M., Wang, S., Lin, W., & Shen, D. G. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00030 · Cited by 51 · see citing works
Xu, Y., Lin, L., Hu, H., Wang, D., Zhu, W., Wang, J., Han, X., & Chen, Y. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1671-9 · Cited by 47 · see citing works
Supervised Learning-based Cancer Detection
Sikder, J., Das, U. K., & Chakma, R. J. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.01205101 · Cited by 45 · see citing works
Bandyk, M., Gopireddy, D. R., Lall, C. G., Balaji, K. C., & Dolz, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104472 · Cited by 44 · see citing works
DenseNet for Anatomical Brain Segmentation
Gottapu, R. D., & Dağli, C. H. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.327 · Cited by 43 · see citing works
A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation
Yan, K., Wang, X., Kim, J., Khadra, M. H., FULHAM, M. J., & Feng, D. D. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.031 · Cited by 42 · see citing works
Deep Learning Applications in Medical Image Analysis
Singha, A., Thakur, R. S., & Patel, T. (2021) · Wiley eBooks · doi:10.1002/9781119711278.ch11 · Cited by 46 · see citing works
Content-based image retrieval for medical diagnosis using fuzzy clustering and deep learning
Sudhish, D. K., Nair, L. R., & Shailesh, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105620 · Cited by 42 · see citing works
Objective assessment of segmentation models for thyroid ultrasound images
Yadav, N., Dass, R., & Virmani, J. (2022) · Journal of Ultrasound · doi:10.1007/s40477-022-00726-8 · Cited by 41 · see citing works
Fast and Adaptive Fractal Tree-Based Path Planning for Programmable Bevel Tip Steerable Needles
Liu, F., Garriga-Casanovas, A., Secoli, R., & y Baena, F. R. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2528292 · Cited by 41 · see citing works
Wang, Z., Meng, Y., Weng, F., Chen, Y., Lu, F., Liu, X., Hou, M., & Zhang, J. (2019) · Annals of Biomedical Engineering · doi:10.1007/s10439-019-02349-3 · Cited by 41 · see citing works
ACEnet: Anatomical context-encoding network for neuroanatomy segmentation
Li, Y., Li, H., & Yong Fan, A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101991 · Cited by 41 · see citing works
Kim, J., Duchin, Y., Shamir, R. R., Patriat, R., Vitek, J. L., Harel, N., & Sapiro, G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24404 · Cited by 40 · see citing works
Zhou, T., Tan, T., Pan, X., Tang, H., & Li, J. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-286 · Cited by 39 · see citing works
Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism
Katuwal, G. J., Baum, S. A., Cahill, N. D., Dougherty, C. C., Evans, E., Evans, D. W., Moore, G. J., & Michael, A. M. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00439 · Cited by 39 · see citing works
Medical Image Analysis Using Artificial Intelligence
Yoon, H. J., Jeong, Y. J., Kang, H. K., Jeong, J. E., & Kang, D. (2019) · Progress in Medical Physics · doi:10.14316/pmp.2019.30.2.49 · Cited by 38 · see citing works
- Authors
- de Brebisson, A., & Montana, G.
- Affiliations
- St Thomas' Hospital (GB)
- ORCID
- Montana, Giovanni
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 20-28
- OpenAlex
- W1910109443
- Cited by
- 330
- References
- 21
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 15 · 2023: 24 · 2022: 37 · 2021: 39 · 2020: 49 · 2019: 55 · 2018: 43 · 2017: 45 · 2016: 12 · 2015: 1
- FWCI
- 13.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- brain segmentation, magnetic resonance imaging, deep neural network, spatial context, multi-scale features
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