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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1488616

Segmentation of Glioblastomas via 3D FusionNet

Provisionally accepted
Xiangyu Guo Xiangyu Guo Botao Zhang Botao Zhang Yue Peng Yue Peng Feng Chen Feng Chen Wenbin Li Wenbin Li *
  • Beijing Tiantan Hospital, Capital Medical University, Beijing, China

The final, formatted version of the article will be published soon.

    This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors. The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD). When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth. FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.

    Keywords: Brain tumor segmentation, MRI, U-net, Segnet, 3D deep learning model

    Received: 30 Aug 2024; Accepted: 29 Oct 2024.

    Copyright: © 2024 Guo, Zhang, Peng, Chen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wenbin Li, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.