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Fueled by recent advances in machine learning, there has been tremendousprogress in the field of semantic segmentation for the medical image computingcommunity. However, developed algorithms are often optimized and validated byhand based on one task only. In combination with small datasets, interpretingthe generalizability of the results is often difficult. The MedicalSegmentation Decathlon challenge addresses this problem, and aims to facilitatedevelopment of generalizable 3D semantic segmentation algorithms that requireno manual parametrization. Such an algorithm was developed and is presented inthis paper. It consists of a 3D convolutional neural network withencoder-decoder architecture employing residual-connections, skip-connectionsand multi-level generation of predictions. It works on anisotropicvoxel-geometries and has anisotropic depth, i.e., the number of downsamplingsteps is a task-specific parameter. These depths are automatically inferred foreach task prior to training. By combining this flexible architecture withon-the-fly data augmentation and little-to-no pre-- or postprocessing,promising results could be achieved. The code developed for this challenge willbe available online after the final deadline at:https://github.com/ORippler/MSD_2018

AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018