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

Yngve Mardal Moe, Aurora Rosvoll Groendahl, Martine Mulstad, Oliver Tomic, Ulf Indahl, Einar Dale, Eirik Malinen, Cecilia Marie Futsaether

15 Apr 2019 (modified: 05 May 2023)MIDL Abstract 2019Readers: Everyone
Keywords: Deep learning, segmentation, delineation, head and neck cancer, HNC, PET/CT, GTV delineation
TL;DR: This work demonstrates that neural networks can achieve close-to-oncologist level delineations in PET/CT images of patients with head and neck cancers.
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 ± 0.17, PET: 0.71 ± 0.12, PET/CT: 0.75 ± 0.12).
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