Large-scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites: Interobserver expert/non-expert and multi-observer composite tumor and normal tissue delineation annotations from a prospective educational challenge

AbstractClinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted data into NIfTI format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the STAPLE method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.