Abstract
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis. It supplies semantic knowledge to visualisation tools for the planning of surgical interventions and the education of health professionals. Fully supervised segmentation of 3D data using Deep Learning methods has been extensively studied for many tasks but is usually restricted to distinguishing only a handful of classes. With 125 distinct bones, our case includes many more labels than typical 3D segmentation tasks. For this reason, the direct adaptation of most established methods is not possible. This paper discusses the intricacies of training a 3D segmentation network in a many-label setting and shows necessary modifications in network architecture, loss function, and data augmentation. As a result, we demonstrate the robustness of our method by automatically segmenting over one hundred distinct bones simultaneously in an end-to-end learnt fashion from a CT-scan.
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References
Baiker, M., et al.: Fully automated whole-body registration in mice using an articulated skeleton atlas. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 728–731. IEEE (2007)
Bilic, P., et al.: The liver tumor segmentation benchmark (lits). arXiv preprint (2019). arXiv:1901.04056
Chaitanya, K., Karani, N., Baumgartner, C.F., Becker, A., Donati, O., Konukoglu, E.: Semi-supervised and task-driven data augmentation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 29–41. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_3
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
Faludi, B., Zoller, E.I., Gerig, N., Zam, A., Rauter, G., Cattin, P.C.: Direct visual and haptic volume rendering of medical data sets for an immersive exploration in virtual reality. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 29–37. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_4
Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)
Fu, Y., Liu, S., Li, H.H., Yang, D.: Automatic and hierarchical segmentation of the human skeleton in CT images. Phys. Med. Biol. 62(7), 2812–2833 (2017)
Gibson, E., et al.: Niftynet: a deep-learning platform for medical imaging. Comput. Methods Programs Biomed. 158, 113–122 (2018)
Horváth, A., Tsagkas, C., Andermatt, S., Pezold, S., Parmar, K., Cattin, P.: Spinal cord gray matter-white matter segmentation on magnetic resonance AMIRA images with MD-GRU. In: Zheng, G., Belavy, D., Cai, Y., Li, S. (eds.) CSI 2018. LNCS, vol. 11397, pp. 3–14. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13736-6_1
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 234–244. Springer International Publishing, Cham (2019)
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint (2014). arXiv:1412.6980
Klein, A., Warszawski, J., Hillengaß, J., Maier-Hein, K.H.: Automatic bone segmentation in whole-body ct images. Int. J. Comput. Assist. Radiol. Surg. 14(1), 21–29 (2019)
Knodel, M.M., et al.: Virtual reality in advanced medical immersive imaging: a workflow for introducing virtual reality as a supporting tool in medical imaging. Comput. Vis. Sci. 18(6), 203–212 (2018). https://doi.org/10.1007/s00791-018-0292-3
Lee, S.W., Kim, J.H., Jun, J., Ha, J.W., Zhang, B.T.: Overcoming catastrophic forgetting by incremental moment matching. In: Advances in neural information processing systems, pp. 4652–4662 (2017)
Lessmann, N., van Ginneken, B., de Jong, P.A., Išgum, I.: Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med. Image Anal. 53, 142–155 (2019)
Lindgren Belal, S., et al.: Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET/CT-based 3D quantification of skeletal metastases. Eur. J. Radiol. 113, 89–95 (2019)
Pérez-Carrasco, J.A., Acha, B., Suárez-Mejías, C., López-Guerra, J.L., Serrano, C.: Joint segmentation of bones and muscles using an intensity and histogram-based energy minimization approach. Comput. Methods Programs Biomed. 156, 85–95 (2018)
Rahbani, D., Morel-Forster, A., Madsen, D., Lüthi, M., Vetter, T.: Robust registration of statistical shape models for unsupervised pathology annotation. In: Zhou, L., et al. (eds.) LABELS/HAL-MICCAI/CuRIOUS -2019. LNCS, vol. 11851, pp. 13–21. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33642-4_2
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sarkalkan, N., Weinans, H., Zadpoor, A.A.: Statistical shape and appearance models of bones. Bone 60, 129–140 (2014)
Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.C.: Automatic segmentation of the pelvic bones from ct data based on a statistical shape model. VCBM 8, 93–100 (2008)
Sekuboyina, A., et al.: Verse: a vertebrae labelling and segmentation benchmark. arXiv preprint (2020). arXiv:2001.09193
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint (2017). arXiv:1710.09412
Acknowledgements
This work was financially supported by the Werner Siemens Foundation through the MIRACLE project. We thank Mireille Toranelli for acquiring the scans and providing the ground truth labelling.
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Schnider, E., Horváth, A., Rauter, G., Zam, A., Müller-Gerbl, M., Cattin, P.C. (2020). 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_5
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