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Multi-Level Parallel Network for Brain Tumor Segmentation

Juhong Tie, Hui Peng*

School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

* Corresponding Author: Hui Peng. Email: email

(This article belongs to this Special Issue: Intelligent Biomedical Image Processing and Computer Vision)

Computer Modeling in Engineering & Sciences 2024, 139(1), 741-757. https://doi.org/10.32604/cmes.2023.043353

Abstract

Accurate automatic segmentation of gliomas in various sub-regions, including peritumoral edema, necrotic core, and enhancing and non-enhancing tumor core from 3D multimodal MRI images, is challenging because of its highly heterogeneous appearance and shape. Deep convolution neural networks (CNNs) have recently improved glioma segmentation performance. However, extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution, resulting in the loss of accurate spatial and object parts information, especially information on the small sub-region tumors, affecting segmentation performance. Hence, this paper proposes a novel multi-level parallel network comprising three different level parallel sub-networks to fully use low-level, mid-level, and high-level information and improve the performance of brain tumor segmentation. We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning. The proposed method is trained and validated on the BraTS 2020 training and validation dataset. On the validation dataset, our method achieved a mean Dice score of 0.907, 0.830, and 0.787 for the whole tumor, tumor core, and enhancing tumor core, respectively. Compared with state-of-the-art methods, the multi-level parallel network has achieved competitive results on the validation dataset.

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Cite This Article

Tie, J., Peng, H. (2024). Multi-Level Parallel Network for Brain Tumor Segmentation. CMES-Computer Modeling in Engineering & Sciences, 139(1), 741–757. https://doi.org/10.32604/cmes.2023.043353



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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