Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features

TL;DR

This paper introduces a modified deep neural network combined with handcrafted features for accurate gland segmentation and malignancy recognition in histopathology images, demonstrating superior performance over existing methods.

Contribution

A novel approach integrating deep networks and handcrafted features for improved gland segmentation and cancer detection in histopathology images.

Findings

Achieved state-of-the-art results on Warwick-QU dataset section B images.

Handcrafted features like local binary pattern significantly enhance segmentation accuracy.

System performs comparably on different image sections, showing robustness.

Abstract

Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images.