College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2022 Mar 23;22(7):2452. doi: 10.3390/s22072452.
Segmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to improve the accuracy of segmentation, this paper proposes a U-Net-based hybrid variable structure-RDCTrans U-Net for liver tumor segmentation in computed tomography (CT) examinations. We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. At the same time, Transformer is introduced in down-sampling to increase the network's overall perception and global understanding of the image and to improve the accuracy of liver tumor segmentation. The method proposed in this paper tests the segmentation performance of liver tumors on the LiTS (Liver Tumor Segmentation) dataset. It obtained 89.22% mIoU and 98.91% Acc, for liver and tumor segmentation. The proposed model also achieved 93.38% Dice and 89.87% Dice, respectively. Compared with the original U-Net and the U-Net model that introduces dense connection, attention mechanism, and Transformer, respectively, the method proposed in this paper achieves SOTA (state of art) results.
医学图像分割是疾病诊断和治疗计划的必要前提。在各种医学图像分割任务中，基于 U-Net 的变体已广泛应用于肝肿瘤分割任务中。鉴于肿瘤形状和大小的高度可变性，为了提高分割的准确性，本文提出了一种基于 U-Net 的混合可变结构-RDCTrans U-Net，用于计算机断层扫描（CT）检查中的肝肿瘤分割。我们设计了一个以 ResNeXt50 为主导的骨干网络，并辅以扩张卷积，以增加网络深度、扩大感知域、提高特征提取效率，而不增加参数。同时，在降采样中引入 Transformer，以增加网络对图像的整体感知和全局理解，提高肝肿瘤分割的准确性。本文提出的方法在 LiTS（Liver Tumor Segmentation）数据集上测试了肝肿瘤的分割性能。它在肝和肿瘤分割方面分别获得了 89.22%的 mIoU 和 98.91%的 Acc。该模型还分别实现了 93.38%的 Dice 和 89.87%的 Dice。与原始的 U-Net 以及分别引入密集连接、注意力机制和 Transformer 的 U-Net 模型相比，本文提出的方法取得了 SOTA（最先进）的结果。
