Abstract
In order to estimate the spinal curvature, we propose two methods to detect the spinal keypoints at first. In Method-1, we use a RetinaNet to predict the bounding box of each vertebra followed by a HR-Net to refine the keypoint detections. In Method-2, we implement a similar two-stage system, which firstly extract 68 rough points along the spine curves using a Simple Baseline. We then generate patches and make sure each of them contains three vertebrae at most based on ground truth. We train a second Simple Baseline to predict the exact keypoints of these patches, which are generally not fixed in numbers. A delicate postprocess of clustering is proposed to deal with dense keypoint predictions due to the freedom of patch selections. After fusing Method-1 and Method-2, we achieve competitive results on the public leaderboard of AASCE2019 challenge.
K. Chen, C. Peng and Y. Li made equal contribution to the project. D. Cheng constructed and directed the research.
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Chen, K., Peng, C., Li, Y., Cheng, D., Wei, S. (2020). Accurate Automated Keypoint Detections for Spinal Curvature Estimation. In: Cai, Y., Wang, L., Audette, M., Zheng, G., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2019. Lecture Notes in Computer Science(), vol 11963. Springer, Cham. https://doi.org/10.1007/978-3-030-39752-4_6
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DOI: https://doi.org/10.1007/978-3-030-39752-4_6
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