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Target segmentation in CT images of Head&Neck (H&N) region is challenging dueto low contrast between adjacent soft tissue. The SegRap 2023 challenge hasbeen focused on benchmarking the segmentation algorithms of NasopharyngealCarcinoma (NPC) which would be employed as auto-contouring tools for radiationtreatment planning purposes. We propose a fully-automatic framework and developtwo models for a) segmentation of 45 Organs at Risk (OARs) and b) two GrossTumor Volumes (GTVs). To this end, we preprocess the image volumes byharmonizing the intensity distributions and then automatically cropping thevolumes around the target regions. The preprocessed volumes were employed totrain a standard 3D U-Net model for each task, separately. Our method tooksecond place for each of the tasks in the validation phase of the challenge.The proposed framework is available at https://github.com/Astarakee/segrap2023

Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma