publication . Other literature type . Preprint . Part of book or chapter of book . 2017

A Deep Level Set Method for Image Segmentation

Min Tang; Sepehr Valipour; Zichen Zhang; Dana Cobzas; Martin Jagersand;
Open Access
  • Published: 17 May 2017
  • Publisher: Springer International Publishing
Abstract
This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is availabl...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 3 versions
http://arxiv.org/pdf/1705.0626...
Part of book or chapter of book
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Part of book or chapter of book . 2017
Provider: Crossref
16 references, page 1 of 2

1. BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: MICCAI (2016) [OpenAIRE]

2. Brosch, T., Yoo, Y., Tang, L.Y., Li, D.K., Traboulsee, A., Tam, R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: MICCAI. pp. 3{11 (2015)

3. Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in mri using graph-based decision fusion on convolutional neural networks. In: MICCAI (2016)

4. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266{277 (2001)

5. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2) (April 2007)

6. Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision 69(3), 335351 (2006)

7. Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Daniel Rueckert and, B.G.: E cient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis 36, 6178 (2017)

8. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. p. 34313440 (2015)

9. Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Medical Image Analysis 35, 159171 (2017)

10. Paragios, N., Deriche, R.: Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. Visual Communication and Image Representation 13, 249{268 (2002)

11. Radau, P.: Cardiac MR Left Ventricle Segmentation Challenge. http:// smial.sri.utoronto.ca/LV_Challenge/Home.html (2008), [Online; accessed 10- December-2016]

12. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI. p. 234241 (2015)

13. Salah, M.B., Mitiche, A., Ayed, I.B.: E ective level set image segmentation with a kernel induced data term. Trans. Img. Proc. 19(1), 220{232 (2010) [OpenAIRE]

14. Van Ginneken, B., Heimann, T., Styner, M.: 3d segmentation in the clinic: A grand challenge. 3D segmentation in the clinic: a grand challenge pp. 7{15 (2007)

15. Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)

16 references, page 1 of 2
Abstract
This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is availabl...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 3 versions
http://arxiv.org/pdf/1705.0626...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book . 2017
Provider: Crossref
16 references, page 1 of 2

1. BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: MICCAI (2016) [OpenAIRE]

2. Brosch, T., Yoo, Y., Tang, L.Y., Li, D.K., Traboulsee, A., Tam, R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: MICCAI. pp. 3{11 (2015)

3. Cai, J., Lu, L., Zhang, Z., Xing, F., Yang, L., Yin, Q.: Pancreas segmentation in mri using graph-based decision fusion on convolutional neural networks. In: MICCAI (2016)

4. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Processing 10(2), 266{277 (2001)

5. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2) (April 2007)

6. Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision 69(3), 335351 (2006)

7. Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Daniel Rueckert and, B.G.: E cient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis 36, 6178 (2017)

8. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. p. 34313440 (2015)

9. Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Medical Image Analysis 35, 159171 (2017)

10. Paragios, N., Deriche, R.: Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. Visual Communication and Image Representation 13, 249{268 (2002)

11. Radau, P.: Cardiac MR Left Ventricle Segmentation Challenge. http:// smial.sri.utoronto.ca/LV_Challenge/Home.html (2008), [Online; accessed 10- December-2016]

12. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI. p. 234241 (2015)

13. Salah, M.B., Mitiche, A., Ayed, I.B.: E ective level set image segmentation with a kernel induced data term. Trans. Img. Proc. 19(1), 220{232 (2010) [OpenAIRE]

14. Van Ginneken, B., Heimann, T., Styner, M.: 3d segmentation in the clinic: A grand challenge. 3D segmentation in the clinic: a grand challenge pp. 7{15 (2007)

15. Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)

16 references, page 1 of 2
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