Department of Anesthesiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, No. 150 Linhai West Street, Taizhou 317000, Zhejiang, China.
Department of Operation, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, No. 150 Linhai West Street, Taizhou 317000, Zhejiang, China.
Contrast Media Mol Imaging. 2022 Mar 20;2022:9322196. doi: 10.1155/2022/9322196. eCollection 2022.
This study was aimed at exploring the application of image segmentation based on full convolutional neural network (FCN) in liver computed tomography (CT) image segmentation and analyzing the clinical features of acute liver injury caused by sepsis. The Sigmoid function, encoder-decoder, and weighted cross entropy loss function were introduced and optimized based on FCN. The Dice value, precision, recall rate, volume overlap error (VOE), relative volume difference (RVD), and root mean square error (RMSE) values of the optimized algorithms were compared and analyzed. 92 patients with sepsis were selected as the research objects, and they were divided into a nonacute liver injury group (50 cases) and acute liver injury group (42 cases) based on whether they had acute liver injury. The differences in the proportion of patients with different disease histories, the proportion of patients with different infection sites, the number of organ failure, and the time of admission to intensive care unit (ICU) were compared between the two groups. It was found that the optimized window CT image Dice value after preprocessing (0.704 ± 0.06) was significantly higher than the other two methods ( < 0.05). The Dice value, precision, and recall rate of the optimized-FCN algorithm were (0.826 ± 0.06), (0.91 ± 0.08), and (0.88 ± 0.09), respectively, which were significantly higher than other algorithms ( < 0.05). The VOE, RVD, and RMSE values were (21.19 ± 1.97), (10.45 ± 1.02), and (0.25 ± 0.02), respectively, which were significantly lower than other algorithms ( < 0.05). The proportion of patients with a history of drinking in the nonacute liver injury group was lower than that in the acute liver injury group ( < 0.05), and the proportion of patients with a history of hypotension was greatly higher than that in the nonacute liver injury group ( < 0.01). CT images of sepsis patients with acute liver injury showed that large areas of liver parenchyma mixed with high-density hematoma, the number of organ failures, and the length of stay in ICU were significantly higher than those in the nonacute liver injury group ( < 0.05). It showed that the optimization algorithm based on FCN greatly improved the performance of CT image segmentation. Long-term drinking, low blood pressure, number of organ failures, and length of stay in ICU were all related to sepsis and acute liver injury. Conclusion in this study could provide a reference basis for the diagnosis and prognosis of acute liver injury caused by sepsis.
本研究旨在探索基于全卷积神经网络（FCN）的图像分割在肝脏计算机断层扫描（CT）图像分割中的应用，并分析脓毒症引起的急性肝损伤的临床特征。本研究在 FCN 的基础上引入并优化了 Sigmoid 函数、编码器-解码器和加权交叉熵损失函数。比较和分析了优化算法的 Dice 值、精度、召回率、体积重叠误差（VOE）、相对体积差异（RVD）和均方根误差（RMSE）值。选择 92 例脓毒症患者为研究对象，根据是否发生急性肝损伤将其分为非急性肝损伤组（50 例）和急性肝损伤组（42 例）。比较两组患者的不同疾病史比例、不同感染部位比例、器官衰竭数量、入住重症监护病房（ICU）时间。结果发现，预处理后优化窗口 CT 图像 Dice 值（0.704±0.06）明显高于其他两种方法（ < 0.05）。优化-FCN 算法的 Dice 值、精度和召回率分别为（0.826±0.06）、（0.91±0.08）和（0.88±0.09），明显高于其他算法（ < 0.05）。VOE、RVD 和 RMSE 值分别为（21.19±1.97）、（10.45±1.02）和（0.25±0.02），明显低于其他算法（ < 0.05）。非急性肝损伤组患者长期饮酒比例低于急性肝损伤组（ < 0.05），低血压史比例明显高于非急性肝损伤组（ < 0.01）。急性肝损伤组脓毒症患者 CT 图像显示，大面积肝实质混合高密度血肿，器官衰竭数量和 ICU 住院时间明显高于非急性肝损伤组（ < 0.05）。这表明基于 FCN 的优化算法极大地提高了 CT 图像分割的性能。长期饮酒、低血压、器官衰竭数量和 ICU 住院时间均与脓毒症和急性肝损伤有关。本研究的结论可为脓毒症引起的急性肝损伤的诊断和预后提供参考依据。
