The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks
Open accessAbstract
Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.
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Cites
U-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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully 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
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
3D 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
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 · cited 3×
“This includes automatic segmentation of brain lesions [2,10], tumors [9,15,21], and neuroanatomy [14,22,3], using voxelwise network architectures [14,9,17], and more recently using 3D voxelwise networks [3,10], and fully convolutional networks (FCNs) [4,13,17].”
BackgroundMethodBrain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
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 · cited 1×
“This includes automatic segmentation of brain lesions [2,10], tumors [9,15,21], and neuroanatomy [14,22,3], using voxelwise network architectures [14,9,17], and more recently using 3D voxelwise networks [3,10], and fully convolutional networks (FCNs) [4,13,17].”
MethodDeep convolutional neural networks for multi-modality isointense infant brain image segmentation
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817
Cited by
Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
Deep semantic segmentation of natural and medical images: a review
Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 870
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102026 · Cited by 733
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662
Boundary loss for highly unbalanced segmentation
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., & Ben Ayed, I. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101851 · Cited by 532
Metadata
- Authors
- Salehi, S. S. M., Erdogmus, D., & Gholipour, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 379-387
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2685298434
- Cited by
- 1,086
- References
- 10
- FWCI
- 10.03
- Type
- conference paper
- Language
- en
- Open access
- green
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