Doğan Kâmil, Selçuk Turab, Alkan Ahmet
Department of Radiology, Kahramanmaras Sutcu Imam University, 46050 Onikişubat, Turkey.
Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, 46050 Onikişubat, Turkey.
Diagnostics (Basel). 2024 May 26;14(11):1102. doi: 10.3390/diagnostics14111102.
Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism within segmental arteries presents greater challenges compared with larger arteries and is frequently overlooked. In this study, we developed a computational method to automatically identify pulmonary embolism within segmental arteries using computed tomography (CT) images. The system architecture incorporates an enhanced Mask R-CNN deep neural network trained on PE-containing images. This network accurately localizes pulmonary embolisms in CT images and effectively delineates their boundaries. This study involved creating a local data set and evaluating the model predictions against pulmonary embolisms manually identified by expert radiologists. The sensitivity, specificity, accuracy, Dice coefficient, and Jaccard index values were obtained as 96.2%, 93.4%, 96.%, 0.95, and 0.89, respectively. The enhanced Mask R-CNN model outperformed the traditional Mask R-CNN and U-Net models. This study underscores the influence of Mask R-CNN's loss function on model performance, providing a basis for the potential improvement of Mask R-CNN models for object detection and segmentation tasks in CT images.
肺栓塞（PE）是指肺动脉被血凝块阻塞，其致死风险约为30%。与较大的动脉相比，段动脉内肺栓塞的检测面临更大挑战，且常被忽视。在本研究中，我们开发了一种计算方法，利用计算机断层扫描（CT）图像自动识别段动脉内的肺栓塞。该系统架构包含一个在含PE图像上训练的增强型Mask R-CNN深度神经网络。该网络能在CT图像中准确定位肺栓塞并有效勾勒其边界。本研究涉及创建一个本地数据集，并将模型预测结果与由放射科专家手动识别的肺栓塞进行评估。灵敏度、特异性、准确率、Dice系数和Jaccard指数值分别为96.2%、93.4%、96%、0.95和0.89。增强型Mask R-CNN模型优于传统的Mask R-CNN和U-Net模型。本研究强调了Mask R-CNN损失函数对模型性能的影响，为CT图像中目标检测和分割任务的Mask R-CNN模型潜在改进提供了依据。
