An annotated fluorescence image dataset for training nuclear segmentation methods.

ABSTRACT: Fully-automated nuclear image segmentation is the prerequisite to ensure statistically significant, quantitative analyses of tissue preparations,applied in digital pathology or quantitative microscopy. The design of segmentation methods that work independently of the tissue type or preparation is complex, due to variations in nuclear morphology, staining intensity, cell density and nuclei aggregations. Machine learning-based segmentation methods can overcome these challenges, however high quality expert-annotated images are required for training. Currently, the limited number of annotated fluorescence image datasets publicly available do not cover a broad range of tissues and preparations. We present a comprehensive, annotated dataset including tightly aggregated nuclei of multiple tissues

SUBMITTER: Kromp F 

PROVIDER: S-EPMC7419523 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature