Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segment

TL;DR

This study compares end-to-end Mask-RCNN and detect-then-segment frameworks for high-resolution kidney tissue image segmentation, finding the latter with DeepLab_v3 performs better at preserving detail and accuracy.

Contribution

The paper introduces a detect-then-segment pipeline with DeepLab_v3 for high-resolution WSI, demonstrating superior performance over traditional end-to-end Mask-RCNN methods.

Findings

Detect-then-segment with DeepLab_v3 achieved 0.953 DSC.

End-to-end Mask-RCNN achieved 0.902 DSC.

Color space (RGB vs LAB) did not significantly affect performance.

Abstract

Automatic instance segmentation of glomeruli within kidney Whole Slide Imaging (WSI) is essential for clinical research in renal pathology. In computer vision, the end-to-end instance segmentation methods (e.g., Mask-RCNN) have shown their advantages relative to detect-then-segment approaches by performing complementary detection and segmentation tasks simultaneously. As a result, the end-to-end Mask-RCNN approach has been the de facto standard method in recent glomerular segmentation studies, where downsampling and patch-based techniques are used to properly evaluate the high resolution images from WSI (e.g., >10,000x10,000 pixels on 40x). However, in high resolution WSI, a single glomerulus itself can be more than 1,000x1,000 pixels in original resolution which yields significant information loss when the corresponding features maps are downsampled via the Mask-RCNN pipeline. In this paper, we assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework. Beyond such a comparison, we also comprehensively evaluate the performance of our detect-then-segment pipeline through: 1) two of the most prevalent segmentation backbones (U-Net and DeepLab_v3); 2) six different image resolutions (from 512x512 to 28x28); and 3) two different color spaces (RGB and LAB). Our detect-then-segment pipeline, with the DeepLab_v3 segmentation framework operating on previously detected glomeruli of 512x512 resolution, achieved a 0.953 dice similarity coefficient (DSC), compared with a 0.902 DSC from the end-to-end Mask-RCNN pipeline. Further, we found that neither RGB nor LAB color spaces yield better performance when compared against each other in the context of a detect-then-segment framework. Detect-then-segment pipeline achieved better segmentation performance compared with End-to-end method.