# Boundary-aware Context Neural Network for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2005.00966 · HTML: https://mlsanity.com/paper/2005.00966/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 18 of 18)

- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Edge-aware Feature Aggregation Network for Polyp Segmentation](https://mlsanity.com/paper/2309.10523/) (2023)
- [LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation](https://mlsanity.com/paper/2409.01572/) (2024)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios](https://mlsanity.com/paper/2601.00537/) (2026)
- [We Care Each Pixel: Calibrating on Medical Segmentation Model](https://mlsanity.com/paper/2503.05107/) (2025)
- [Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation](https://mlsanity.com/paper/2505.04652/) (2025)
- [Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer](https://mlsanity.com/paper/2408.00347/) (2024)
- [A new baseline for edge detection: Make Encoder-Decoder great again](https://mlsanity.com/paper/2409.14976/) (2024)
- [SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots](https://mlsanity.com/paper/2301.08157/) (2023)
- [DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation](https://mlsanity.com/paper/2311.00483/) (2023)
- [PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion](https://mlsanity.com/paper/2312.08323/) (2023)
- [Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint assessment in shoulder CT scans](https://mlsanity.com/paper/2410.12641/) (2024)
- [FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentation](https://mlsanity.com/paper/2601.01687/) (2026)
- [CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation](https://mlsanity.com/paper/2603.19659/) (2026)
- [UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image](https://mlsanity.com/paper/2608.27240/) (2026)

## Abstract (arXiv)

Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. The performance of medical image segmentation has been significantly advanced with the convolutional neural networks (CNNs). However, most existing CNNs-based methods often produce unsatisfactory segmentation mask without accurate object boundaries. This is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. In that the medical image is characterized by the high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation are still challenging today. In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information. BA-Net adopts encoder-decoder architecture. In each stage of encoder network, pyramid edge extraction module is proposed for obtaining edge information with multiple granularities firstly. Then we design a mini multi-task learning module for jointly learning to segment object masks and detect lesion boundaries. In particular, a new interactive attention is proposed to bridge two tasks for achieving information complementarity between different tasks, which effectively leverages the boundary information for offering a strong cue to better segmentation prediction. At last, a cross feature fusion module aims to selectively aggregate multi-level features from the whole encoder network. By cascaded three modules, richer context and fine-grain features of each stage are encoded. Extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art approaches.