Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei, China.
Inflamm Bowel Dis. 2024 Nov 4;30(11):1957-1964. doi: 10.1093/ibd/izad285.
The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity.
This was a retrospective study with 2 sets of CTE image data. Segmentation datasets were used to establish nnU-Net neural network's automatic segmentation model. The classification dataset was processed using the automatic segmentation model to obtain segmentation results and extract radiomics features. The most optimal features were then selected to build 5 machine learning classifiers to distinguish CD activity. The performance of the automatic segmentation model was evaluated using the Dice similarity coefficient, while the performance of the machine learning classifier was evaluated using the area under the curve, sensitivity, specificity, and accuracy.
The segmentation dataset had 84 CTE examinations of CD patients (mean age 31 ± 13 years, 60 males), and the classification dataset had 193 (mean age 31 ± 12 years, 136 males). The deep learning segmentation model achieved a Dice similarity coefficient of 0.824 on the testing set. The logistic regression model showed the best performance among the 5 classifiers in the testing set, with an area under the curve, sensitivity, specificity, and accuracy of 0.862, 0.697, 0.840, and 0.759, respectively.
The automated segmentation model accurately segments CD lesions, and machine learning classifier distinguishes CD activity well. This method can assist radiologists in promptly and precisely evaluating CD activity.
本文旨在开发一种深度学习自动分割模型，用于对 CT 肠造影（CTE）图像中的克罗恩病（CD）病变进行分割。此外，还将分析从分割的 CD 病变中提取的放射组学特征，并构建多个机器学习分类器来区分 CD 活动。
这是一项回顾性研究，包含 2 组 CTE 图像数据。分割数据集用于建立 nnU-Net 神经网络自动分割模型。分类数据集经过自动分割模型处理，以获得分割结果并提取放射组学特征。然后选择最佳特征来构建 5 个机器学习分类器，以区分 CD 活动。自动分割模型的性能通过 Dice 相似系数进行评估，而机器学习分类器的性能通过曲线下面积、敏感性、特异性和准确性进行评估。
分割数据集包含 84 例 CD 患者的 CTE 检查（平均年龄 31±13 岁，60 名男性），分类数据集包含 193 例（平均年龄 31±12 岁，136 名男性）。深度学习分割模型在测试集中的 Dice 相似系数达到 0.824。逻辑回归模型在测试集中 5 个分类器中表现最好，曲线下面积、敏感性、特异性和准确性分别为 0.862、0.697、0.840 和 0.759。
自动分割模型能准确分割 CD 病变，机器学习分类器能很好地区分 CD 活动。该方法可帮助放射科医生快速准确地评估 CD 活动。
