CNN Cascades for Segmenting Whole Slide Images of the Kidney

TL;DR

This paper introduces CNN cascade architectures tailored for segmenting sparse structures in whole slide kidney images, achieving high accuracy and efficiency in glomeruli segmentation compared to traditional methods.

Contribution

The study presents novel CNN cascade models specifically designed for sparse object segmentation in histopathology, outperforming standard fully-convolutional networks in accuracy and speed.

Findings

Achieved a Dice coefficient of 0.90 for glomeruli segmentation.

Cascade CNNs outperform single CNNs and conventional methods.

Proposed approaches are computationally efficient.

Abstract

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.