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In the era of open science, public datasets, along with common experimentalprotocol, help in the process of designing and validating data sciencealgorithms; they also contribute to ease reproductibility and fair comparisonbetween methods. Many datasets for image segmentation are available, eachpresenting its own challenges; however just a very few exist for radiotherapyplanning. This paper is the presentation of a new dataset dedicated to thesegmentation of organs at risk (OARs) in the thorax, i.e. the organssurrounding the tumour that must be preserved from irradiations duringradiotherapy. This dataset is called SegTHOR (Segmentation of THoracic Organsat Risk). In this dataset, the OARs are the heart, the trachea, the aorta andthe esophagus, which have varying spatial and appearance characteristics. Thedataset includes 60 3D CT scans, divided into a training set of 40 and a testset of 20 patients, where the OARs have been contoured manually by anexperienced radiotherapist. Along with the dataset, we present some baselineresults, obtained using both the original, state-of-the-art architecture U-Netand a simplified version. We investigate different configurations of thisbaseline architecture that will serve as comparison for future studies on theSegTHOR dataset. Preliminary results show that room for improvement is left,especially for smallest organs.

SegTHOR: Segmentation of Thoracic Organs at Risk in CT images