NCL-SM：来自人类骨骼肌活检图像的完整标注数据集
NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
AI总结：
发布NCL-SM数据集，包含46个人类骨骼肌横截面及超5万手动分割肌纤维，支持开发自动化精准分割与分类流程。
摘要：
人类骨骼肌（SM）组织横截面的单细胞分析是理解许多神经肌肉疾病的基本工具。为了使该分析可靠且可重复，SM组织显微图像中单个纤维的识别（分割）应当是自动且精确的。该领域的生物医学科学家目前依赖定制工具和通用机器学习（ML）模型，随后进行劳动密集且主观的手动干预以微调分割。我们相信，通过训练ML模型，完全自动化、精确且可重复的分割是可行的。然而，在这一重要的生物医学领域，目前尚无高质量、公开可用的标注成像数据集用于ML模型训练。本文发布NCL-SM：一个高质量生物成像数据集，包含来自健康对照受试者和遗传诊断肌肉病理患者的46个人类SM组织横截面。这些图像包含超过50,000个手动分割的肌纤维（myofibres）。此外，我们还整理了高质量的肌纤维分割，标注了在SM组织图像中拒绝低质量肌纤维和低质量区域的原因，使这些标注完全可用于下游分析。我们相信，这将为开发全自动流程奠定基础，该流程可在组织切片图像中识别单个肌纤维，并特别对适合进一步分析的肌纤维进行分类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
