Segmentation of the human tongue musculature using MRI: Field guide and validation in motor neuron disease

作者：Thomas B. Shaw, Fernanda L. Ribeiro, Xiangyun Zhu, Patrick Aiken, Saskia Bollmann, Steffen Bollmann, Jeryn Chang, Kali Chidley, Harriet Dempsey‐Jones, Zeinab Eftekhari, Jennifer Gillespie, Robert D. Henderson, Matthew C. Kiernan, Sofia Ira Ktena, Pamela A. McCombe, Shyuan T. Ngo, Shana Taubert, Brooke‐Mai Whelan, Xincheng Ye, Frederik J. Steyn, Sicong Tu, Markus Barth · 发表于：Computers in Biology and Medicine · 年份：2025 · DOI：10.1016/j.compbiomed.2025.110824 · 被引用次数：4 · 研究领域：Voice and Speech Disorders、Dysphagia Assessment and Management、Neurological disorders and treatments

This work addresses the challenge of reliably measuring the muscles of the human tongue, which are difficult to quantify due to complex interwoven muscle types. We introduce a new semi-automated method, enabled by a manually curated dataset of MRI scans to accurately measure five key tongue muscles, combining AI-assisted, atlas-based, and manual segmentation approaches. The method was tested and validated in a dataset of 178 scans and included segmentation validation (n = 103) and clinical application (n = 132) in individuals with motor neuron disease. We show that people with speech and swallowing deficits tend to have smaller muscle volumes and present a normalisation strategy that removes confounding demographic factors, enabling broader application to large MRI datasets. As the tongue is generally covered in neuroimaging protocols, our multi-contrast pipeline will allow for the post-hoc analysis of a vast number of datasets. We expect this work to enable the investigation of tongue muscle morphology as a marker in a wide range of diseases that implicate tongue function, including neurodegenerative diseases and pathological speech disorders.