College of Mathematics and Statistics, Southwest University, Chongqing 400715, China.
Department of Military Logistics, Army Logistic University of PLA, Chongqing 401331, China.
Comput Intell Neurosci. 2022 Jun 16;2022:3045370. doi: 10.1155/2022/3045370. eCollection 2022.
The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liver tumors. Fifty-eight patients with liver tumors were selected, and their CT images were segmented using a convolutional neural network (CNN) algorithm. The segmentation results were quantitatively evaluated using the Dice similarity coefficient (DSC), precision, and recall. All the patients were examined and diagnosed by CT enhanced delayed scan technique, and the CT scan results were compared with the pathological findings. The results showed that the DSC, precision, and recall of the CNN algorithm reached 0.987, 0.967, and 0.954, respectively. The images segmented by the CNN were clearer. The diagnostic result of the examination on 56 cases by CT enhanced delay scanning was consistent with that of pathological diagnosis. According to the result of pathological diagnosis, there were 6 cases with hepatic cyst, 9 with hepatic hemangioma, 12 cases with liver metastasis, 10 cases with hepatoblastoma, 3 cases with focal nodular hyperplasia, and 18 cases with primary liver cancer. The result of CT enhanced delay scanning on 58 patients was consistent with that of pathological diagnosis, and the total diagnostic coincidence rate reached 96.55%. In conclusion, the CNN algorithm can perform accurate and efficient segmentation, with high resolution, providing a more scientific basis for the segmentation of liver tumors in CT images. CT enhanced scanning technology has a good effect on the diagnosis and differentiation of liver tumor patients, with high diagnostic coincidence rate. It has important value for the diagnosis of liver tumor and is worthy of clinical application.
本研究旨在探讨基于深度学习的计算机断层扫描（CT）图像在肝脏肿瘤诊断中的应用价值。选取 58 例肝脏肿瘤患者，采用卷积神经网络（CNN）算法对其 CT 图像进行分割。采用 Dice 相似系数（DSC）、准确率和召回率对分割结果进行定量评估。所有患者均采用 CT 增强延迟扫描技术进行检查和诊断，并将 CT 扫描结果与病理发现进行比较。结果显示，CNN 算法的 DSC、准确率和召回率分别达到 0.987、0.967 和 0.954。CNN 分割的图像更清晰。56 例 CT 增强延迟扫描检查的诊断结果与病理诊断一致。根据病理诊断结果，肝囊肿 6 例，肝血管瘤 9 例，肝转移 12 例，肝母细胞瘤 10 例，局灶性结节性增生 3 例，原发性肝癌 18 例。58 例患者的 CT 增强延迟扫描结果与病理诊断一致，总诊断符合率达到 96.55%。综上所述，CNN 算法能够实现准确高效的分割，具有较高的分辨率，为 CT 图像中肝脏肿瘤的分割提供了更科学的依据。CT 增强扫描技术对肝脏肿瘤患者的诊断和鉴别具有良好的效果，诊断符合率高，对肝脏肿瘤的诊断具有重要价值，值得临床应用。
