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Single cell analysis of human skeletal muscle (SM) tissue cross-sections is afundamental tool for understanding many neuromuscular disorders. For thisanalysis to be reliable and reproducible, identification of individual fibreswithin microscopy images (segmentation) of SM tissue should be automatic andprecise. Biomedical scientists in this field currently rely on custom tools andgeneral machine learning (ML) models, both followed by labour intensive andsubjective manual interventions to fine-tune segmentation. We believe thatfully automated, precise, reproducible segmentation is possible by training MLmodels. However, in this important biomedical domain, there are currently nogood quality, publicly available annotated imaging datasets available for MLmodel training. In this paper we release NCL-SM: a high quality bioimagingdataset of 46 human SM tissue cross-sections from both healthy control subjectsand from patients with genetically diagnosed muscle pathology. These imagesinclude $>$ 50k manually segmented muscle fibres (myofibres). In addition wealso curated high quality myofibre segmentations, annotating reasons forrejecting low quality myofibres and low quality regions in SM tissue images,making these annotations completely ready for downstream analysis. This, webelieve, will pave the way for development of a fully automatic pipeline thatidentifies individual myofibres within images of tissue sections and, inparticular, also classifies individual myofibres that are fit for furtheranalysis.

NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies