# Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features

arXiv: https://arxiv.org/abs/1909.00270 · HTML: https://mlsanity.com/paper/1909.00270/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology](https://mlsanity.com/paper/2301.13128/) (2023)

## Abstract (arXiv)

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.