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7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.
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This content will become publicly available on April 14, 2026
Explainsegnet: Interpretable Segmentation for Alzheimer's Diagnosis
This analysis utilizes a residual 3D convolutional neural network (equipped with 4 attention layers). The core contributions of our work are twofold: firstly, we have innovatively integrated clinical expertise into the initialization of the attention layer’s weights through whole-brain segmentation technique; secondly, we have employed various state-of-the-art model interpretation techniques. These techniques effectively annotate influential brain regions and demonstrate promising results within neuroimaging analysis, as reflected in the key metrics and outcomes. Our findings underscore the potential of deep learning in neuroimaging, especially highlighting the critical role of comprehensive brain segmentation in enhancing diagnostic accuracy.
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- Award ID(s):
- 2238700
- PAR ID:
- 10608605
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-2052-6
- Page Range / eLocation ID:
- 01 to 05
- Subject(s) / Keyword(s):
- 3D Structural MRI, Whole-Brain Segmentation, CNN, Model Interpretation
- Format(s):
- Medium: X
- Location:
- Houston, TX, USA
- Sponsoring Org:
- National Science Foundation
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