L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation
This addresses the problem of inconsistent training and performance bottlenecks in medical image segmentation for practitioners, though it appears incremental.
The paper tackled large scale variations in medical image segmentation by proposing a differentiable two-stage network architecture, which outperformed state-of-the-art coarse-to-fine models with negligible computation overheads on a public dataset.
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.