Gland Segmentation in Histopathological Images by Deep Neural Network

TL;DR

This paper presents a deep neural network approach based on LinkNet for gland segmentation in challenging histopathological images, improving accuracy by edge enhancement and hematoxylin component use.

Contribution

The study introduces a novel application of LinkNet for gland segmentation and explores the impact of different loss functions and image enhancements.

Findings

Comparable to state-of-the-art methods on Warwick-Qu dataset

Edge enhancement improves segmentation accuracy

Hematoxylin component use enhances model performance

Abstract

Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By using Warwick-Qu dataset, which contains two test sets and one train set, we show that our approach is comparable to state-of-the-art methods. Finally, it is shown that enhancing the gland edges and the use of hematoxylin components can improve the performance of the proposed model.