Cross-platform dataset of multiplex fluorescent cellular object image annotations.

ABSTRACT: Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platf

SUBMITTER: Aleynick N 

PROVIDER: S-EPMC10082189 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature