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/wholebrainSeg.

翻译：基于磁共振成像的全脑分割技术能够无创测量脑区，包括颅内总体积和后颅窝体积。在现有全脑分割方法论中整合颅内测量，可提升脑结构分析的全面性。尽管该技术潜力巨大，但由于缺乏同时包含全脑与TICV/PFV标注的有限人工标注图谱，深度学习技术在颅内测量泛化应用中面临数据可用性限制。本文通过增强用于全脑分割的分层级Transformer UNesT，实现同时完成133类全脑分割与TICV/PFV测量。针对数据稀缺问题，模型首先在来自8个不同站点的4859个T1加权三维体素数据上进行预训练。这些体素通过多图谱分割流水线生成标签，但TICV/PFV标签不可用。随后，模型在开放存取系列影像研究（OASIS）的45个T1w三维体素数据上微调，该数据集同时包含133类全脑标签和TICV/PFV标签。我们采用Dice相似系数评估方法，结果表明模型在保持132个脑区性能相当的前提下，实现了精准的TICV/PFV估计。代码与训练模型见：https://github.com/MASILab/UNesT/wholebrainSeg。