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Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers

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arxiv 1908.00841 v1 pith:NF6GNXA5 submitted 2019-08-02 eess.IV

Yngve Mardal Moe , Aurora Rosvoll Groendahl , Martine Mulstad , Oliver Tomic , Ulf Indahl , Einar Dale , Eirik Malinen , Cecilia Marie Futsaether This is my paper · ORCID
classification eess.IV
keywords imagesalgorithmpatientsautomaticcancerscoefficientdicehead
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abstract

An automatic segmentation algorithm for delineation of the gross tumour volume and pathologic lymph nodes of head and neck cancers in PET/CT images is described. The proposed algorithm is based on a convolutional neural network using the U-Net architecture. Several model hyperparameters were explored and the model performance in terms of the Dice similarity coefficient was validated on images from 15 patients. A separate test set consisting of images from 40 patients was used to assess the generalisability of the algorithm. The performance on the test set showed close-to-oncologist level delineations as measured by the Dice coefficient (CT: $0.65 \pm 0.17$, PET: $0.71 \pm 0.12$, PET/CT: $0.75 \pm 0.12$).

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  1. MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble

    physics.med-ph 2024-12 conditional novelty 3.0 of 10

    Using nnU-Net V2 with a 15-fold ensemble, the authors report aggregated Dice scores of 0.81 (pre-RT) and 0.70 (mid-RT) for head and neck tumor segmentation on MRI.

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