A large annotated medical image dataset for the development and evaluation of segmentation algorithms
This provides a standardized resource for researchers in medical imaging to develop and evaluate segmentation methods, addressing a data bottleneck in the field.
The authors tackled the lack of high-quality annotated medical image datasets for semantic segmentation by creating a large, open-source collection of ten labeled datasets from various anatomies, which was used in a crowd-sourced challenge to benchmark algorithms.
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.