Abstract
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due to minimal and blurred bone surface response in US images, cross-machine discrepancy, imaging artifacts, and low signal-to-noise ratio. Notably, bone shadows are caused by a significant acoustic impedance mismatch between the soft tissue and bone surfaces. To leverage these complementary features between these highly related tasks, we propose a single end-to-end network with a shared transformer-based encoder and task independent decoders for simultaneous bone and shadow segmentation. To share complementary features, we propose a cross task feature transfer block which learns to transfer meaningful features from decoder of shadow segmentation to that of bone segmentation and vice-versa. We also introduce a correspondence consistency loss which makes sure that network utilizes the inter-dependency between the bone surface and its corresponding shadow to refine the segmentation. Validation against expert annotations shows that the method outperforms the previous state-of-the-art for both bone surface and shadow segmentation.
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References
Alsinan, A., Vives, M., Patel, V., Hacihaliloglu, I.: Spine surface segmentation from ultrasound using multi-feature guided CNN. CAOS 3, 6–10 (2019)
Alsinan, A.Z., Patel, V.M., Hacihaliloglu, I.: Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int. J. Comput. Assist. Radiol. Surg. 14(5), 775–783 (2019)
Alsinan, A.Z., Patel, V.M., Hacihaliloglu, I.: Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN. Int. J. Comput. Assist. Radiol. Surg. 15(9), 1477–1485 (2020)
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Graham, B., et al.: LeViT: a vision transformer in convnet’s clothing for faster inference. arXiv preprint arXiv:2104.01136 (2021)
Hacihaliloglu, I.: Enhancement of bone shadow region using local phase-based ultrasound transmission maps. Int. J. Comput. Assisted Radiol. Surg. 12(6), 951–960 (2017)
Hacihaliloglu, I.: Ultrasound imaging and segmentation of bone surfaces: a review. Technology 5(02), 74–80 (2017)
Hacihaliloglu, I., Rasoulian, A., Rohling, R.N., Abolmaesumi, P.: Local phase tensor features for 3-D ultrasound to statistical shape+ pose spine model registration. IEEE Trans. Med. Imaging 33(11), 2167–2179 (2014)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, P., Patel, V.M., Hacihaliloglu, I.: Simultaneous segmentation and classification of bone surfaces from ultrasound using a multi-feature guided CNN. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 134–142. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_16
Wang, P., Vives, M., Patel, V.M., Hacihaliloglu, I.: Robust bone shadow segmentation from 2D ultrasound through task decomposition. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 805–814. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_78
Wang, P., Vives, M., Patel, V.M., Hacihaliloglu, I.: Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN. Int. J. Comput. Assist. Radiol. Surg. 15, 1127–1135 (2020)
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Rahman, A., Valanarasu, J.M.J., Hacihaliloglu, I., Patel, V.M. (2022). Simultaneous Bone and Shadow Segmentation Network Using Task Correspondence Consistency. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_32
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DOI: https://doi.org/10.1007/978-3-031-16440-8_32
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