NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle
  Biopsies
Atif Khan Conor Lawless Amy Vincent Charlotte Warren Valeria Di Leo Tiago Gomes A. Stephen McGough
Single cell analysis of human skeletal muscle (SM) tissue cross-sections 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 automatic and
precise. Biomedical scientists in this field currently rely on custom tools and
general machine learning (ML) models, both followed by labour intensive and
subjective manual interventions to fine-tune segmentation. We believe that
fully automated, precise, reproducible segmentation is possible by training ML
models. However, in this important biomedical domain, 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 SM tissue cross-sections from both 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 myofibre segmentations, annotating reasons for
rejecting low quality myofibres and low quality regions in SM tissue images,
making these annotations 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.