Meng Qingxu, Wang Weijiang, Qi Hang, Dang Hua, Jia Minli, Wang Xiaohua
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100086, China.
Sci Rep. 2025 Jun 5;15(1):19816. doi: 10.1038/s41598-025-03151-z.
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource distribution. Convolutional neural networks (CNNs) perform extremely well in processing local detailed features. However, there restricted receptive field renders them incapable of capturing global context information. Although the combination of CNNs and Transformers balances the ability to capture local detailed features and global context information, it inevitably increases the model's parameters and computational cost, which restricts its equal deployment in real medical scenarios. To address this issue, We propose a Lightweight Network with Roberts edge enhancement (LR-Net) for brain tumor segmentation that achieves an optimal balance between parameters and diagnostic accuracy. We propose a 3D Spatial Shift Convolution and Pixel Shuffle (SSCPS) module, the SSCPS module present a low-parameter, low-computational-cost spatial shift convolution that overcomes the limitation of receptive field and improves the ability to extract global contextual information, Pixel Shuffle (PS) module extracts spatial information from feature dimensions, efficiently replacing traditional upsampling module. The Channel Dilation Mechanism in SSCPS module dynamically adjust the number of output channels to maintain the range and depth of network feature aggregation. Additionally, the network leverages a combination of Channel Attention and Roberts Edge Enhancement (CAREE) module, to improve the channel aggregation capability and sensitivity of fuzzy boundaries. Our method achieved Dice of 0.806, 0.881, and 0.860 in BraTS2019, BraTS2020, and BraTS2021 datasets, while the parameters is only 4.72 M, which is only 3.03% of UNETR's and 28.92% of UNet3D's. This balance of efficiency and accuracy makes the proposed network well-suited for practical clinical applications.
在临床医学中，一种可靠且资源友好的脑肿瘤分割计算机辅助诊断（CAD）方法对于提高诊断准确性和治疗效果至关重要，特别是在医疗资源分布不均衡的地区。卷积神经网络（CNN）在处理局部细节特征方面表现出色。然而，其受限的感受野使其无法捕捉全局上下文信息。尽管CNN与Transformer的结合平衡了捕捉局部细节特征和全局上下文信息的能力，但不可避免地增加了模型参数和计算成本，这限制了其在实际医疗场景中的平等部署。为了解决这个问题，我们提出了一种具有罗伯茨边缘增强的轻量级网络（LR-Net）用于脑肿瘤分割，该网络在参数和诊断准确性之间实现了最佳平衡。我们提出了一种3D空间移位卷积和像素洗牌（SSCPS）模块，该模块呈现出低参数、低计算成本的空间移位卷积，克服了感受野的限制并提高了提取全局上下文信息的能力，像素洗牌（PS）模块从特征维度中提取空间信息，有效地替代了传统的上采样模块。SSCPS模块中的通道扩张机制动态调整输出通道数量，以保持网络特征聚合的范围和深度。此外，该网络利用通道注意力和罗伯茨边缘增强（CAREE）模块的组合，提高通道聚合能力和模糊边界的敏感性。我们的方法在BraTS2019、BraTS2020和BraTS2021数据集中分别达到了0.806、0.881和0.860的Dice系数，而参数仅为4.72M，仅为UNETR的3.03%和UNet3D的28.92%。这种效率和准确性的平衡使得所提出的网络非常适合实际临床应用。
