  Single cell analysis of skeletal muscle (SM) tissue is a fundamental tool for
understanding many neuromuscular disorders. For this analysis to be reliable
and reproducible, identification of individual fibres within microscopy images
(segmentation) of SM tissue should be precise. There is currently no tool or
pipeline that makes automatic and precise segmentation and curation of images
of SM tissue cross-sections possible. Biomedical scientists in this field rely
on custom tools and general machine learning (ML) models, both followed by
labour intensive and subjective manual interventions to get the segmentation
right. We believe that automated, precise, reproducible segmentation is
possible by training ML models. However, there are currently no good quality,
publicly available annotated imaging datasets available for ML model training.
In this paper we release NCL-SM: a high quality bioimaging dataset of 46 human
tissue sections from healthy control subjects and from patients with
genetically diagnosed muscle pathology. These images include $>$ 50k manually
segmented muscle fibres (myofibres). In addition we also curated high quality
myofibres and annotated reasons for rejecting low quality myofibres and regions
in SM tissue images, making this data completely ready for downstream analysis.
This, we believe, will pave the way for development of a fully automatic
pipeline that identifies individual myofibres within images of tissue sections
and, in particular, also classifies individual myofibres that are fit for
further analysis.
