A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps

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Version: Final published version
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ID Serval
serval:BIB_B8862FBB2AD5
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps
Titre de la conférence
Proceedings of Machine Learning Research
Auteur⸱e⸱s
Nguyen Huu-Giao, Pica Alessia, Hrbacek Jan, Weber Damien C., La Rosa Francesco, Schalenbourg Ann, Sznitman Raphael, Bach Cuadra Meritxell
Organisation
Medical Imaging with Deep Learning London, 8 ‑ 10 July 2019
Statut éditorial
Publié
Date de publication
08/07/2019
Peer-reviewed
Oui
Volume
102
Série
Proceedings of Machine Learning Research, 2019
Pages
370–379
Langue
anglais
Résumé
An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocular cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual labels are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning.
Mots-clé
Activation map, CAM, Unet, tumor segmentation, Uveal melanoma
Création de la notice
05/02/2021 17:53
Dernière modification de la notice
06/02/2021 8:10
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