# DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

arXiv: https://arxiv.org/abs/1604.02677 · HTML: https://mlsanity.com/paper/1604.02677/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2016.

## Built on by (most cited first, 30 of 84)

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- [Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2107.11787/) (2021)
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- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [Transformer based multiple instance learning for weakly supervised histopathology image segmentation](https://mlsanity.com/paper/2205.08878/) (2022)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Unleashing the Power of Prompt-driven Nucleus Instance Segmentation](https://mlsanity.com/paper/2311.15939/) (2023)
- [A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation](https://mlsanity.com/paper/2106.07557/) (2021)
- [TA-Net: Topology-Aware Network for Gland Segmentation](https://mlsanity.com/paper/2110.14593/) (2021)
- [DoNet: Deep De-overlapping Network for Cytology Instance Segmentation](https://mlsanity.com/paper/2303.14373/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [TransNuSeg: A Lightweight Multi-Task Transformer for Nuclei Segmentation](https://mlsanity.com/paper/2307.08051/) (2023)
- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [Contour Proposal Networks for Biomedical Instance Segmentation](https://mlsanity.com/paper/2104.03393/) (2021)
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- [A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model](https://mlsanity.com/paper/2507.17303/) (2025)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation](https://mlsanity.com/paper/2211.14491/) (2022)
- [Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2305.00678/) (2023)
- [Advanced Deep Networks for 3D Mitochondria Instance Segmentation](https://mlsanity.com/paper/2104.07961/) (2021)
- [DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions](https://mlsanity.com/paper/2404.14956/) (2024)
- [Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation](https://mlsanity.com/paper/2402.14611/) (2024)
- [Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology](https://mlsanity.com/paper/2310.20427/) (2023)
- [Federated Learning via Input-Output Collaborative Distillation](https://mlsanity.com/paper/2312.14478/) (2023)
- [SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation](https://mlsanity.com/paper/2401.09773/) (2024)

## Abstract (arXiv)

The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.