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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation
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A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

机译：学习群体卷积的深度学习模型和脑肿瘤细分的深度监督

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摘要

The segmentation of brain tumors in medical images is a crucial step of clinical treatment. Manual segmentation is time consuming and labor intensive, and existing automatic segmentation methods suffer from issues such as numerous parameters and low precision. To resolve these issues, this study proposes a learnable group convolution-based segmentation method that replaces convolution in the feature extraction stage with learnable group convolution, thereby reducing the number of convolutional network parameters and enhancing communication between convolution groups. To improve utilization of the feature maps, we added a skip connection structure between learnable group convolution modules, which increased segmentation precision. We used deep supervision to combine output images in the network output stage to reduce overfitting and enhance the recognition capabilities of the network. We tested the proposed algorithm model using the open BraTS 2018 dataset. The experiment results revealed that the proposed model is superior to 3D U-Net and DMFNet and has better segmentation results for tumor cores than No New-Net and NVDLMED, the winning methods in the BraTS 2018 challenge. The segmentation precision of the proposed method with regard to whole tumors, enhancing tumors, and tumor cores was 90.25%, 80.36%, and 86.20%. Furthermore, the proposed method uses fewer parameters and a less complex model.
机译：医学图像中脑肿瘤的分割是临床治疗的关键步骤。手动分割是耗时和劳动密集型，现有的自动分段方法遭受众多参数和低精度等问题。要解决这些问题，本研究提出了一种基于学习的组卷积的分段方法，该分割方法将具有学习组群卷积的特征提取阶段的卷积替换，从而减少了卷积网络参数的数量并增强了卷积组之间的通信。为了提高特征映射的利用率，我们在学习组卷积模块之间添加了跳过连接结构，这增加了分割精度。我们使用深度监督将输出图像组合在网络输出级中，以减少过度装备并增强网络的识别功能。我们使用Open Brats 2018 DataSet测试了所提出的算法模型。实验结果表明，所提出的模型优于3D U-Net和DMFNET，并且对于肿瘤核和NVDLMED的肿瘤核心具有更好的分段结果，这是Brats 2018挑战中的获胜方法。所提出的方法关于全肿瘤，增强肿瘤和肿瘤核的分割精度为90.25％，80.36％和86.20％。此外，所提出的方法使用更少的参数和较差的模型。

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