ArticleMedical image analysis2021
ACEnet: Anatomical context-encoding network for neuroanatomy segmentation.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- 2D, 2.5D, or 3D? Comparing Dimensional Approaches in Deep Neural Networks for 3D Medical Image Analysis.Journal of imaging informatics in medicine · 2026Review
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.Magnetic resonance in medicine · 2024Article
- Article
- HNAS-Reg: Hierarchical Neural Architecture Search for Deformable Medical Image Registration.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Article
- Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm.Physical and engineering sciences in medicine · 2022Article
- MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.Sensors (Basel, Switzerland) · 2022Article
- Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.Medical image analysis · 2021Article
- Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.Medical image analysis · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.