Abstract
Multi-organ segmentation in abdominal Computed Tomography (CT) images is of great importance for diagnosis of abdominal lesions and subsequent treatment planning. Though deep learning based methods have attained high performance, they rely heavily on large-scale pixel-level annotations that are time-consuming and labor-intensive to obtain. Due to its low dependency on annotation, weakly supervised segmentation has attracted great attention. However, there is still a large performance gap between current weakly-supervised methods and fully supervised learning, leaving room for exploration. In this work, we propose a novel 3D framework with two consistency constraints for scribble-supervised multiple abdominal organ segmentation from CT. Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with one encoder and three decoders using different dilation rates to capture features from different receptive fields that are complementary to each other to generate high-quality soft pseudo labels. For more stable unsupervised learning, we use voxel-wise uncertainty to rectify the soft pseudo labels and then supervise the outputs of each decoder. To further regularize the network, class relationship information is exploited by encouraging the generated class affinity matrices to be consistent across different decoders under multi-view projection. Experiments on the public WORD dataset show that our method outperforms five existing scribble-supervised methods.
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References
Chen, Q., Hong, Y.: Scribble2D5: weakly-supervised volumetric image segmentation via scribble annotations. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 234–243. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_23
Chen, X., et al.: A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother. Oncol. 160, 175–184 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_43
Dai, J., He, K., Sun, J.: Boxsup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: ICCV, pp. 1635–1643 (2015)
En, Q., Guo, Y.: Annotation by clicks: a point-supervised contrastive variance method for medical semantic segmentation. arXiv preprint arXiv:2212.08774 (2022)
Gao, F., et al.: Segmentation only uses sparse annotations: unified weakly and semi-supervised learning in medical images. Med. Image Anal. 80, 102515 (2022)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NeurIPS, pp. 1–17 (2004)
Javanmardi, M., Sajjadi, M., Liu, T., Tasdizen, T.: Unsupervised total variation loss for semi-supervised deep learning of semantic segmentation. arXiv preprint arXiv:1605.01368 (2016)
Kim, B., Ye, J.C.: Mumford-shah loss functional for image segmentation with deep learning. IEEE Trans. Image Process. 29, 1856–1866 (2019)
Liang, Z., Wang, T., Zhang, X., Sun, J., Shen, J.: Tree energy loss: towards sparsely annotated semantic segmentation. In: CVPR, pp. 16907–16916 (2022)
Lin, D., Dai, J., Jia, J., He, K., Sun, J.: Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: CVPR, pp. 3159–3167 (2016)
Liu, X., et al.: Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images. Pattern Recogn. 122, 108341 (2022)
Luo, X.: WSL4MIS (2021). https://github.com/Luoxd1996/WSL4MIS
Luo, X., et al.: Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 528–538. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_50
Luo, X., et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 318–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_30
Luo, X., et al.: WORD: a large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. Med. Image Anal. 82, 102642 (2022)
Obukhov, A., Georgoulis, S., Dai, D., Van Gool, L.: Gated CRF loss for weakly supervised semantic image segmentation. arXiv preprint arXiv:1906.04651 (2019)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS, vol. 32 (2019)
Ru, L., Zhan, Y., Yu, B., Du, B.: Learning affinity from attention: end-to-end weakly-supervised semantic segmentation with transformers. In: CVPR, pp. 16846–16855 (2022)
Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised CNN segmentation. In: CVPR, pp. 1818–1827 (2018)
Tang, M., Perazzi, F., Djelouah, A., Ben Ayed, I., Schroers, C., Boykov, Y.: On regularized losses for weakly-supervised CNN segmentation. In: ECCV, pp. 507–522 (2018)
Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: ICCV, pp. 1365–1374 (2019)
Wang, Y., Zhou, Y., Shen, W., Park, S., Fishman, E.K., Yuille, A.L.: Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med. Image Anal. 55, 88–102 (2019)
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation. In: CVPR, pp. 7268–7277 (2018)
Zhang, K., Zhuang, X.: Cyclemix: a holistic strategy for medical image segmentation from scribble supervision. In: CVPR, pp. 11656–11665 (2022)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62271115), Science and Technology Department of Sichuan Province, China (2022YFSY0055) and Radiation Oncology Key Laboratory of Sichuan Province Open Fund (2022ROKF04).
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Han, M., Luo, X., Liao, W., Zhang, S., Zhang, S., Wang, G. (2023). Scribble-Based 3D Multiple Abdominal Organ Segmentation via Triple-Branch Multi-Dilated Network with Pixel- and Class-Wise Consistency. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_4
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