Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage.
Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage.
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智能分割算法结合血清学指标早期预测脑出血后血肿扩大
DOI：
10.1155/2022/5863082
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发表时间：
2022
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
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本研究旨在探讨基于智能分割算法和血清学指标的脑CT图像在脑出血（ICH）患者血肿扩大早期预测中的应用价值。引入模糊c均值（FCM）智能分割算法，选取150例早期脑出血患者作为研究对象。对患者脑CT图像进行智能分割，评估该算法的诊断价值。根据CT检查时血肿大小的不同，将患者分为观察组（发生血肿增大，n = 48）和对照组（未发生血肿增大，n = 102）。通过评估两组患者的CT图像质量，测量两组患者脑水肿、血肿体积及血清学指标，探讨脑出血后血肿增大的预测价值。结果表明，智能分割算法处理的CT图像灵敏度为0.894，特异度为0.898，准确率为0.930。观察组患者早期水肿增大、血肿明显高于对照组。相对水肿体积为0.912，明显低于对照组（1.017）（P < 0.05）。脑出血患者CT征象方面，观察组混合征象、低密度征象、脑卒中发生率均明显高于对照组（P < 0.05）。观察组患者淋巴细胞绝对计数（ALC）和血红蛋白（HGB）浓度分别为6.23 × 109/L和6.29 × 109/L，均高于对照组（6.08 × 109/L和4.25 × 109/L）。中性粒细胞与淋巴细胞比值（NLR）为0.99 × 109/L，显著低于对照组（1.43 × 109/L） （P < 0.05）。综上所述，FCM算法处理后的脑CT图像对脑出血的诊断效果较好，对脑出血患者血肿的早期预测具有较高的临床价值。
The aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy C-means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to different hematoma volumes during CT examination, patients were divided into observation group (hematoma enlargement occurred, n = 48) and control group (no hematoma enlargement occurred, n = 102). The predicative value of hematoma enlargement after ICH was investigated by assessing CT image quality and measuring intracerebral edema, hematoma volume, and serological indicators of the patients of the two groups. The results demonstrated that the sensitivity, specificity, and accuracy of CT images processed by intelligence segmentation algorithm amounted to 0.894, 0.898, and 0.930, respectively. Besides, early edema enlargement and hematoma of patients in the observation group were more significant than those of patients in the control group. Relative edema volume was 0.912, which was apparently lower than that in the control group (1.017) (P < 0.05). In terms of CT signs of ICH patients, the incidence of blend sign, low density sign, and stroke of the observation group was evidently higher than those of the control group (P < 0.05). Besides, absolute lymphocyte count (ALC) and hemoglobin (HGB) concentration of the patients in the observation group were 6.23 × 109/L and 6.29 × 109/L, respectively, both of which were higher than those of the control group (6.08 × 109/L and 4.25 × 109/L). Neutrophil to lymphocyte ratio (NLR) was 0.99 × 109/L, which was apparently lower than that in the control group (1.43 × 109/L) (P < 0.05). To sum up, cerebral CT images processed by FCM algorithm showed good diagnostic effect on ICH and high clinical values in the early prediction of hematoma among ICH patients.
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影响因子：
1.9
作者：
Li, Hui;Xie, Yuanliang;Jiang, Xiaoli
通讯作者：
Jiang, Xiaoli
影响因子：
5.3
作者：
Sembill, Jochen A.;Kuramatsu, Joji B.;Huttner, Hagen B.
通讯作者：
Huttner, Hagen B.
影响因子：
2.8
作者：
Li, Hui;Xie, Yuanliang;Wang, Xiang
通讯作者：
Wang, Xiang
DOI：
10.1016/j.jstrokecerebrovasdis.2021.105946
发表时间：
2021-06-29
影响因子：
2.5
作者：
Muscari，Antonio;Masetti，Giovanni;Zoli，Marco
通讯作者：
Zoli，Marco
影响因子：
4.3
作者：
Hu M;Zhong Y;Xie S;Lv H;Lv Z
通讯作者：
Lv Z