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該論文發(fā)表于期刊Biomedical Signal Processing and Control，根據(jù)腦膠質多序列MR影像的特性，設計了一種并行多通道與特征融合的深度學習網(wǎng)絡，實驗結果證明該網(wǎng)絡在同時分割腫瘤壞死區(qū)、增強區(qū)和水腫區(qū)三個重要區(qū)域，均具有優(yōu)異的性能。
Glioma segmentation is a crucial task for accurate quantitative analysis, precise diagnosis and effective
treatment. However, the challenge remains in simultaneously locating multiple sub-regions within the tumor
from multi-sequence MRI images, as they exhibit heterogeneous appearance, shape, diverse sizes, locations,
and different intensities. The mainstream approach currently used is to use a single network to simultaneously
perform segmentation of gliomas and their internal regions. However, this approach overlooks the differences
between segmentation tasks, leading to the shared use of all intermediate features for all tasks. The effectiveness
of certain features for a specific task relies on the learning of network parameters, lacking structural division.
As a result, the overall performance of joint image segmentation is compromised. To address this issue, a
multi-task parallel with feature sharing integrated 3D U-Nets is proposed, which employs three sub-networks
to identify three sub-regions of the tumor. Each sub-network is dedicated to identifying a specific sub-region of
the tumor, thus dividing various types of features required by different tasks into different sub-networks, and
avoiding mutual interference between tasks. The sub-networks incorporate special feature sharing pathways in
the encoder, allowing them to capture the spatial inclusion relationship and intensity continuity among the subregions.
Furthermore, a compound loss function is defined, as a weighted sum of a Dice loss and a cross entropy
loss to achieve a tradeoff between alleviating class imbalance and promoting training smoothness. Ablation
studies have shown the effectiveness of parallel structure and feature sharing pathways in the proposed method.
Experimental results and comparisons on quantitative and qualitative evaluation demonstrate the proposed
method is superior to classical and state-of-the-art methods. On the BraTS 2021 dataset, the proposed method
achieves the combined optimization of segmentation performance, such as the average Dice, PPV, Sensitivity,
and HD95 over multiple regions achieves the best of 0.908, 0.923, 0.931, 3.312.
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