今天分享一篇发表在CVPR2020上的关于医学图像处理的论文:Deep Distance Transform for Tubular Structure Segmentation in CT Scans (原文链接:[1])。
医学图像中的管状结构分割(如CT扫描中的血管分割)是使用计算机辅助早期筛查相关疾病的重要步骤。但是目前CT扫描中管状结构的自动分割由于存在对比度差、噪声大、背景复杂等问题而仍然存在很大的挑战。同时,如下图(Figure 1)所示,管状结构其实可以由一系列圆心和半径不断变化的球体组成的。受此启发,这篇文章尝试将这一几何特点融入到管状结构的分割任务中,以提高其分割结果的准确性。
这篇文章主要有如下贡献:
如上图(Figure 2)所示,DDT在训练阶段和测试阶段有些许差异。
[1] http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Deep_Distance_Transform_for_Tubular_Structure_Segmentation_in_CT_Scans_CVPR_2020_paper.pdf
[2] Fethallah Benmansour and Laurent D. Cohen. Tubular structure segmentation based on minimal path method and anisotropic enhancement. International Journal of Computer Vision, 92(2):192–210, 2011.
[3] Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, and Alan L. Yuille. Training multi-organ segmentation networks with sample selection by relaxed upper confident bound. In Proc. MICCAI, 2018.
[4] Rasmus Rothe, Radu Timofte, and Luc Van Gool. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision, 126(2-4):144–157, 2018.
[5] Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai, and Alan L. Yuille. Deepskeleton: Learning multi-task scale-associated deep side outputs for object skeleton extraction in natural images. IEEE Trans. Image Processing, 26(11):5298–5311, 2017.
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