Accurately positioning and segmenting thymoma from computed tomography (CT) images is of great importance for an image-driven thymoma analysis. In clinical practice, the diagnosis and segmentation of thymomas for radiologists are time-consuming and inefficient tasks. Thus, it is necessary to develop a method to accurately and efficiently realize automatic segmentation of thymoma. Here, a dense skip connection encoding–decoding model (DSC-Net), which is a deep convolutional neural network, was proposed to perform automatic segmentation of thymoma with the ability to fuse feature maps under receptive fields of different scales. An image preprocessing method was also proposed to provide much more texture information and enhance the contrast between thymoma and its surrounding tissues. A total of 310 subjects who underwent contrast-enhanced CT scanning were included in this ethically-approved retrospective study. All of the CT slices were manually labeled by four experienced radiologists, and 80% of images were included in the training set and the rest were included in the testing set. The performance of segmentation was evaluated by calculating the accuracy, intersection over union (IoU), and Boundary F1 contour matching score (BFScore) between the predicted segmentation and the manual labels. For segmentation of thymoma in the testing set, the accuracy, IoU and BFScore were 92.96%, 87.86% and 0.9087 respectively. Compared to the U-Net method, the DSC-Net model improved IoU by 3.94%. In addition, the efficacy and robustness of DSC-Net in segmentation of different patients and different types of thymoma classified by the WHO histological classification criteria were verified. The proposed preprocessing method and DSC-Net demonstrated improved performance in segmentation of thymomas, suggesting the ability to provide consistent delineation and assist radiologists in their clinical applications.

从计算机断层扫描（CT）图像准确定位和分割胸腺瘤对于图像驱动的胸腺瘤分析非常重要。在临床实践中，放射科医生对胸腺瘤的诊断和分割是耗时且低效的任务。因此，有必要开发一种方法来准确有效地实现胸腺瘤的自动分割。在这里，提出了一种密集的跳过连接编码/解码模型（DSC-Net），它是一种深度卷积神经网络，可以对胸腺瘤进行自动分割，并能够融合不同尺度下的感受野下的特征图。还提出了一种图像预处理方法，以提供更多的纹理信息并增强胸腺瘤及其周围组织之间的对比度。这项经伦理学批准的回顾性研究共纳入了310位接受了增强CT扫描的受试者。所有CT切片均由四名经验丰富的放射科医生手动标记，并且训练集中包括了80％的图像，而测试集中包括了其余图像。分割的性能通过计算预测的分割和手动标签之间的准确度，联合相交（IoU）和边界F1轮廓匹配分数（BFScore）进行评估。对于胸腺瘤在测试集中的分割，准确度，IoU和BFScore分别为92.96％，87.86％和0.9087。与U-Net方法相比，DSC-Net模型将IoU提高了3.94％。此外，验证了DSC-Net在按WHO组织学分类标准分类的不同患者和不同类型胸腺瘤的分割中的有效性和鲁棒性。拟议的预处理方法和DSC-Net证明了胸腺瘤分割方面的改进性能，表明了能够提供一致的轮廓并协助放射科医生进行临床应用的能力。