Gland Instance Segmentation by Deep Multichannel Side Supervision

TL;DR

This paper introduces DMCS, a deep learning framework that improves gland instance segmentation in histology images by fusing multichannel information with side supervision, achieving state-of-the-art results.

Contribution

The paper presents a novel deep multichannel side supervision method that automatically exploits complex features for gland segmentation, reducing manual feature design.

Findings

Achieves state-of-the-art performance on gland segmentation benchmarks.

Effectively fuses regional and boundary information through side supervision.

Reduces the need for manual feature engineering in gland segmentation.

Abstract

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics.