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Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

arXiv 2309.04071 ↗First posted 8 Sep 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/tree/main/wholebrainSeg.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 8 Sep 2023.

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References · 14

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  9. 2023Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentationcited by 12
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  11. 2018CompNet: Complementary Segmentation Network for Brain MRI Extractioncited by 9
  12. 2022Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Modelscited by 7
2 more
  1. 2022Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentationcited by 5
  2. 2022Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Trainingcited by 1

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