D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation
D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation
复制标题
D-UNet：用于慢性中风病变分割的维度融合U形网络
DOI：
10.1109/tcbb.2019.2939522
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发表时间：
2021-05-01
影响因子：
4.5
通讯作者：
Wang, Shanshan
中科院分区：
文献类型：
--
作者：
Zhou, Yongjin;Huang, Weijian;Wang, Shanshan
评估慢性卒中造成的损害的位置和程度对于医学诊断、手术计划和预后至关重要。近年来，随着二维和三维卷积神经网络(CNN)的快速发展，编解码器结构在医学图像分割领域显示出巨大的潜力。然而，二维细胞神经网络忽略了医学图像的三维信息，而三维细胞神经网络对计算资源的要求较高。本文提出了一种在编码阶段创新性地将二维和三维卷积结合在一起的新的结构，称为维度-融合-非标准编码(D-UNET)。与2D网络相比，该结构获得了更好的分割性能，而与3D网络相比，所需的计算时间显著减少。此外，为了缓解网络训练中正负样本之间的数据不平衡问题，我们提出了一种新的损失函数，称为增强混合损失(EML)。该函数增加了一个加权焦距系数，并结合了两个传统的损失函数。提出的方法已经在ATLAS数据集上进行了测试，并与三种最先进的方法进行了比较。实验结果表明，该方法在差值控制=0.5349+0.2763，精度=0.6331+0.295的条件下获得了最好的质量性能。
Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation. However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN suffers from high computational resource demands. This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The proposed architecture achieves a better segmentation performance than 2D networks, while requiring significantly less computation time in comparison to 3D networks. Furthermore, to alleviate the data imbalance issue between positive and negative samples for the network training, we propose a new loss function called Enhance Mixing Loss (EML). This function adds a weighted focal coefficient and combines two traditional loss functions. The proposed method has been tested on the ATLAS dataset and compared to three state-of-the-art methods. The results demonstrate that the proposed method achieves the best quality performance in terms of DSC = 0.5349 + 0.2763 and precision = 0.6331 + 0.295).