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

Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu
10/4/2023
eess.IVcs.CV

Official code: Astarakee/segrap2023 (5 stars, last commit 2023-12-18).

Abstract

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

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Code Implementations(1)

Astarakee/segrap2023Official100%

Codes of model wining the SegRap MICCAI challenge 2023.

51Python, DockerfileJul 20, 20232 years ago

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Cite this paper

@article{astaraki2023fully,
  title  = {Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma},
  author = {Mehdi Astaraki and Simone Bendazzoli and Iuliana Toma-Dasu},
  year   = {2023},
  eprint = {2310.02972},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.02972v1}
}

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