3D Kidney Segmentation in MRI using Transformers

Motivation: Convolutional Neural Networks (CNNs) have long been the go-to deep-learning architecture for medical image segmentation, but in recent years transformer-based architectures adapted from large language models are setting a new standard. Goal(s): The aim of this study was to test if transformers are suitable for 3D kidney segmentation on high-resolution MRI. Approach: A transformer-based deep-learning architecture (UNETR) was trained and tested against a supervised method on 82 patient datasets from the iBEAt study on diabetic kidney disease. Results: UNETR provides fast segmentation with comparable results to the supervised method, but additional refinement is needed to reduce the limits of agreement. Impact: Novel transformer-based architectures for medical image segmentation may be useful for fast 3D segmentation of individual kidneys.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0725
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
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article

3D Kidney Segmentation in MRI using Transformers

N. Grenier, María Gómez, Bixente Dilharreguy, Andrew Brown et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced Neural Network Applications
article

3D Kidney Segmentation in MRI using Transformers

N. Grenier, María Gómez, Bixente Dilharreguy, Andrew Brown, David Shelley, Francesco Santini, Kywe Kywe Soe, João Periquito, Angela C. Shore, Kanishka Sharma, Kim M. Gooding, Steven Sourbron, Jonathan Fulford, Bashair Alhummiany, Mark Gilchrist
article en

Abstract

Motivation: Convolutional Neural Networks (CNNs) have long been the go-to deep-learning architecture for medical image segmentation, but in recent years transformer-based architectures adapted from large language models are setting a new standard. Goal(s): The aim of this study was to test if transformers are suitable for 3D kidney segmentation on high-resolution MRI. Approach: A transformer-based deep-learning architecture (UNETR) was trained and tested against a supervised method on 82 patient datasets from the iBEAt study on diabetic kidney disease. Results: UNETR provides fast segmentation with comparable results to the supervised method, but additional refinement is needed to reduce the limits of agreement. Impact: Novel transformer-based architectures for medical image segmentation may be useful for fast 3D segmentation of individual kidneys.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 79%
Advanced Neural Network Applications
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