ArticleFrontiers in neuroimaging2022
Improving across-dataset brain tissue segmentation for MRI imaging using transformer.
Article in Frontiers in neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.Medical physics · 2026Article
- Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps.Brain communications · 2026Article
- Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.NMR in biomedicine · 2025Review
- nnSegNeXt: A 3D Convolutional Network for Brain Tissue Segmentation Based on Quality Evaluation.Bioengineering (Basel, Switzerland) · 2024Article
- ENHANCING TRANSCRANIAL FOCUSED ULTRASOUND TREATMENT PLANNING WITH SYNTHETIC CT FROM ULTRA-SHORT ECHO TIME (UTE) MRI: A MULTI-TASK DEEP LEARNING APPROACH.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Article
- Improving across-dataset brain tissue segmentation for MRI imaging using transformer.Frontiers in neuroimaging · 2022Article
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Authors and funding
9 authors.
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Abstract
Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our method is inherently robust and can serve as a valuable tool for brain related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS.
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