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Automatic localization and segmentation of organs-at-risk (OAR) in CT areessential pre-processing steps in medical image analysis tasks, such asradiation therapy planning. For instance, the segmentation of OAR surroundingtumors enables the maximization of radiation to the tumor area withoutcompromising the healthy tissues. However, the current medical workflowrequires manual delineation of OAR, which is prone to errors and isannotator-dependent. In this work, we aim to introduce a unified 3D pipelinefor OAR localization-segmentation rather than novel localization orsegmentation architectures. To the best of our knowledge, our proposedframework fully enables the exploitation of 3D context information inherent inmedical imaging. In the first step, a 3D multi-variate regression networkpredicts organs' centroids and bounding boxes. Secondly, 3D organ-specificsegmentation networks are leveraged to generate a multi-organ segmentation map.Our method achieved an overall Dice score of $0.9260\pm 0.18 \%$ on theVISCERAL dataset containing CT scans with varying fields of view and multipleorgans.

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning