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.