Introducing NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
This addresses the problem for biomedical scientists studying neuromuscular disorders by providing a foundational resource to reduce labor-intensive manual work, though it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the lack of high-quality annotated datasets for automated segmentation of skeletal muscle tissue images by releasing NCL-SM, a dataset with 46 human tissue sections and over 50k manually segmented muscle fibers, which enables the development of fully automatic pipelines for precise and reproducible analysis.
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.