# Gland Instance Segmentation Using Deep Multichannel Neural Networks

arXiv: https://arxiv.org/abs/1611.06661 · HTML: https://mlsanity.com/paper/1611.06661/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [TA-Net: Topology-Aware Network for Gland Segmentation](https://mlsanity.com/paper/2110.14593/) (2021)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction](https://mlsanity.com/paper/2207.01791/) (2022)
- [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://mlsanity.com/paper/2208.12460/) (2022)
- [Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform](https://mlsanity.com/paper/2310.05262/) (2023)
- [Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping](https://mlsanity.com/paper/2307.11989/) (2023)
- [DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation](https://mlsanity.com/paper/2312.07584/) (2023)
- [Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention](https://mlsanity.com/paper/2401.15990/) (2024)
- [A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images](https://mlsanity.com/paper/2503.19407/) (2025)
- [O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation](https://mlsanity.com/paper/2506.11913/) (2025)
- [SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations](https://mlsanity.com/paper/2609.23019/) (2026)

## Abstract (arXiv)

Objective: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also be individually identified. Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images. Our proposed algorithm, a deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirements by altering channels. Results: Compared with methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-of-the-art results based on the evaluation metrics. Conclusion: The proposed deep multichannel algorithm is an effective method for gland instance segmentation. Significance: The generalization ability of our model not only enable the algorithm to solve gland instance segmentation problems, but the channel is also alternative that can be replaced for a specific task.