The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical
image processing task, the various challenges have been organized to focus only on specific
clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical
image analysis challenge, in which algorithms compete in a multitude of both tasks and
modalities to investigate the hypothesis that a method capable of performing well on multiple
tasks will generalize well to a previously unseen task and potentially outperform a customdesigned solution. MSD results confirmed this hypothesis, moreover, MSD winner continued
generalizing well to a wide range of other clinical problems for the next two years. Three main
conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the
training of accurate AI segmentation models is now commoditized to scientists that are not
versed in AI model training.