L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation

• URL: http://arxiv.org/abs/2102.05971v1
• Date: Thu, 11 Feb 2021 12:29:39 GMT
• Title: L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation
• Authors: Jiahao Xie, Sheng Zhang, Jianwei Lu, Ye Luo
• Abstract summary: We propose a differentiable two-stage network architecture to tackle these problems. In the first stage, a localization network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in the second stage, a segmentation network (S-Net) performs fine segmentation on the recalibrated RoIs. Experimental results on the public dataset show that our method outperforms state-of-the-art coarse-to-fine models with negligible overheads.
• Score: 6.351506259996282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation. However, those methods have two primary limitations: the first-stage segmentation becomes a performance bottleneck; the lack of overall differentiability makes the training process of two stages asynchronous and inconsistent. In this paper, we propose a differentiable two-stage network architecture to tackle these problems. In the first stage, a localization network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in the second stage, a segmentation network (S-Net) performs fine segmentation on the recalibrated RoIs; a RoI recalibration module between L-Net and S-Net eliminating the inconsistencies. Experimental results on the public dataset show that our method outperforms state-of-the-art coarse-to-fine models with negligible computation overheads.

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