Improving Across-Dataset Brain Tissue Segmentation Using Transformer Article Swipe

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Vishwanatha M. Rao , Zihan Wan , David J. Ma , Pin-Yu Lee , Ye Tian , Andrew F. Laine , Jia Guo · YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2201.08741

Brain tissue segmentation has demonstrated great utility in quantifying MRI data through Voxel-Based Morphometry and highlighting subtle structural changes associated with various conditions within the brain. However, manual segmentation is highly labor-intensive, and automated approaches have struggled due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. Despite the recent success of deep convolutional neural networks (CNNs) for brain tissue segmentation, many such solutions do not generalize well to new datasets, which is critical for a reliable solution. Transformers have demonstrated success in natural image segmentation and have recently been applied to 3D medical image segmentation tasks due to their ability to capture long-distance relationships in the input where the local receptive fields of CNNs struggle. This study introduces a novel CNN-Transformer hybrid architecture designed for brain tissue segmentation. We validate our model's performance across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, time points, and neuropsychiatric conditions. In all situations, our model achieved the greatest generality and reliability. Out 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.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2201.08741

PDF

https://arxiv.org/pdf/2201.08741

OA Status

green

Cited By

1

Related Works

10

OpenAlex ID

https://openalex.org/W4221151536

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OpenAlex ID

https://openalex.org/W4221151536

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DOI

https://doi.org/10.48550/arxiv.2201.08741

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Title

Improving Across-Dataset Brain Tissue Segmentation Using Transformer

Work title

Type

preprint

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Language

en

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Publication year

2022

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Publication date

2022-01-21

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Authors

Vishwanatha M. Rao, Zihan Wan, David J. Ma, Pin-Yu Lee, Ye Tian, Andrew F. Laine, Jia Guo

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Landing page

https://arxiv.org/abs/2201.08741

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PDF URL

https://arxiv.org/pdf/2201.08741

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2201.08741

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Concepts

Segmentation, Computer science, Artificial intelligence, Convolutional neural network, Voxel, Pattern recognition (psychology), Deep learning, Artificial neural network, Image segmentation, Computer vision

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Cited by

1

Total citation count in OpenAlex

Citations by year (recent)

2022: 1

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Related works (count)

10

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