Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks

Abstract : Volume segmentation is one of the most time consuming and therefore error prone tasks in the field of medicine. The construction of a good segmentation requires cross-validation from highly trained professionals. In order to address this problem we propose the use of 3D deep convolutional networks (DCN). Using a 2 step procedure we first segment whole the tumor from a low resolution volume and then feed a second step which makes the fine tissue segmentation. The advantages of using 3D-DCN is that it extracts 3D features form all neighbouring voxels. In this method all parameters are self-learned during a single training procedure and its accuracy can improve by feeding new examples to the trained network. The training dice-loss value reach 0.85 and 0.9 for the coarse and fine segmentation networks respectively. The obtained validation and testing mean dice for the Whole Tumor class are 0.86 and 0.82 respectively.
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https://hal-utt.archives-ouvertes.fr/hal-02276147
Contributor : Daniel Gavrysiak <>
Submitted on : Monday, September 2, 2019 - 12:34:18 PM
Last modification on : Monday, September 16, 2019 - 4:36:07 PM

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Ramiro German Rodríguez Colmeiro, C. Verrastro, Thomas Grosges. Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017., Sep 2017, Quebec City, Canada. pp.226-240, ⟨10.1007/978-3-319-75238-9_20⟩. ⟨hal-02276147⟩

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