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

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arxiv 2310.02972 v1 pith:ZBUIPSAJ submitted 2023-10-04 eess.IV cs.CV

Mehdi Astaraki , Simone Bendazzoli , Iuliana Toma-Dasu This is my paper · ORCID
classification eess.IVcs.CV
keywords segmentationvolumestargetcarcinomachallengeemployedframeworkgross
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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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  1. Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

    eess.IV 2024-11 conditional novelty 4.0 of 10

    A challenge report showing a MedNeXt small model winning Task 1 (DSC 0.8254) and an nnUNet ensemble placing 8th in Task 2 (0.7005) for MRI-guided radiotherapy tumor segmentation.

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