Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk
  for Radiotherapy Planning of Nasopharyngeal Carcinoma
Mehdi Astaraki Simone Bendazzoli Iuliana Toma-Dasu
Target segmentation in CT images of Head&Neck (H&N) region is challenging due
to low contrast between adjacent soft tissue. The SegRap 2023 challenge has
been focused on benchmarking the segmentation algorithms of Nasopharyngeal
Carcinoma (NPC) which would be employed as auto-contouring tools for radiation
treatment planning purposes. We propose a fully-automatic framework and develop
two models for a) segmentation of 45 Organs at Risk (OARs) and b) two Gross
Tumor Volumes (GTVs). To this end, we preprocess the image volumes by
harmonizing the intensity distributions and then automatically cropping the
volumes around the target regions. The preprocessed volumes were employed to
train a standard 3D U-Net model for each task, separately. Our method took
second place for each of the tasks in the validation phase of the challenge.
The proposed framework is available at https://github.com/Astarakee/segrap2023