College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Department of Computer Science, State University of New York, New Paltz, New York 12561, USA.
Comput Math Methods Med. 2022 Mar 9;2022:1248311. doi: 10.1155/2022/1248311. eCollection 2022.
As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.
由于没有对比增强，非增强 MRI 中的肝肿瘤区域边缘模糊，对比度低，这极大地影响了肝肿瘤诊断的速度和准确性。因此，精确分割非增强 MRI 中的肝肿瘤成为一个紧迫而具有挑战性的任务。在本文中，我们提出了一种基于边缘约束和定位映射分割模型（ECLMS），以从非增强 MRI 中准确分割肝肿瘤。它由两部分组成：定位网络和双分支分割网络。我们构建了定位网络，该网络生成先验粗掩码，为分割网络提供位置映射。这部分增强了模型在非增强图像中定位肝肿瘤的能力。我们设计了一个双分支分割网络，其中主解码分支专注于肿瘤核心区域的特征表示，边缘解码分支集中于捕获肿瘤的边缘信息。为了提高模型捕获详细特征的能力，引入了 sSE 块和密集上采样连接。我们设计了瓶颈多尺度模块，使用不同大小的核构建多尺度特征表示，同时集成肿瘤的位置映射。我们在一个包含 215 个不同个体的私有非增强 MRI 数据集上对 ECLMS 模型进行了评估。该模型在 Dice 系数、精度和准确率方面取得了最佳结果，分别为 90.23%、92.25%和 92.39%。实验结果证明了我们模型的有效性，与现有的分割方法相比，我们的模型在非增强肝肿瘤的分割任务中取得了更好的结果。
