# ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation

arXiv: https://arxiv.org/abs/2002.05773 · HTML: https://mlsanity.com/paper/2002.05773/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation](https://mlsanity.com/paper/2311.10696/) (2023)
- [A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention](https://mlsanity.com/paper/2405.00130/) (2024)
- [Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model](https://mlsanity.com/paper/2207.02399/) (2022)
- [SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images](https://mlsanity.com/paper/2303.02922/) (2023)
- [MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation](https://mlsanity.com/paper/2401.11856/) (2024)
- [AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation](https://mlsanity.com/paper/2202.13687/) (2022)
- [HNAS-reg: hierarchical neural architecture search for deformable medical image registration](https://mlsanity.com/paper/2308.12440/) (2023)
- [CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation](https://mlsanity.com/paper/2506.22882/) (2025)
- [Retinal Layer Segmentation in OCT Images With 2.5D Cross-slice Feature Fusion Module for Glaucoma Assessment](https://mlsanity.com/paper/2603.24115/) (2026)

## Abstract (arXiv)

Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation. In order to overcome this limitation, we develop an Anatomical Context-Encoding Network (ACEnet) to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans, consisting of 1) an anatomical context encoding module to incorporate anatomical information in 2D CNNs and 2) a spatial context encoding module to integrate 3D image information in 2D CNNs. In addition, a skull stripping module is adopted to guide the 2D CNNs to attend to the brain. Extensive experiments on three benchmark datasets have demonstrated that our method achieves promising performance compared with state-of-the-art alternative methods for brain structure segmentation in terms of both computational efficiency and segmentation accuracy.