ArticleMedical image analysis2023
UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 91 citations in OpenAlex.
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- Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models.Sensors (Basel, Switzerland) · 2026Review
- Distilling population specific expertise into a unified model for generalizable brain tumor segmentation.Scientific reports · 2026Article
- A 3D feature fusion model integrating multi-scale MRI feature for interpretable Glioblastoma prediction.Medical & biological engineering & computing · 2026Article
- Phenotype discovery of traumatic brain injury segmentations from heterogeneous multi-site data.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- MedPTQ: a practical pipeline for real post-training quantization in 3D medical image segmentation.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2025Review
- Data-Oriented Octree Inverse Hierarchical Order Aggregation Hybrid Transformer-CNN for 3D Medical Segmentation.Journal of imaging informatics in medicine · 2025Article
- Top-k Bottom All butDiagnostics (Basel, Switzerland) · 2025Article
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- Comparison and calibration of MP2RAGE quantitative T1 values to multi-TI inversion recovery T1 values.Magnetic resonance imaging · 2025Article
- Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- A 4D atlas of diffusion-informed spatial smoothing windows for BOLD signal in white matter.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
- Assessing the role of volumetric brain information in multiple sclerosis progression.Computational and structural biotechnology journal · 2025Article
- Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: a multi-institutional study on coronary computed tomography angiography images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks. Our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively. Code, pre-trained models, and use case pipeline are available at: https://github.com/MASILab/UNesT.
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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.