Zhao Jie-Yi, Zhou Zheng-Song, Wang Xiao-Yu, Zhang Hao-Yu, Duan Zong-Hao, Wang Shun-Min, Wan Hong-Li, Zhang Tao
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China.
Chengdu Jincheng College, Chengdu 611731, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2022 May;53(3):511-516. doi: 10.12182/20220560202.
To establish a brain hematoma CT image segmentation method based on watershed and region-growing algorithm so as to measure hematoma volume quickly and accurately, to explore the consistency between the results of this segmentation method and those of manual segmentation, the clinical gold standard, and to compare the results of this method with the calculation of the two Tada formulas commonly used in clinical practice.
The preoperative CT images of 152 patients who were treated for spontaneous cerebral hemorrhage at the Department of Neurosurgery, West China Hospital, Sichuan University between January 2018 and June 2019 were retrospectively collected. The CT images were randomly assigned, by using a random number table, to the training set, the test set and the validation set, which contained 100 patients, 22 patients and 30 patients, respectively. The labeling results of the training set and the test set were used in algorithm training and testing. Four methods, namely, manual segmentation, algorithm segmentation, i.e., segmentation calculation based on watershed and regional growth algorithm, Tada formula, i.e., the traditional Tada formula calculation, and accurate Tada formula, i.e., accurate Tada formula calculation based on 3D-Slicer, were applied on the validation set to measure the hematoma volume. The Digital Imaging and Communications in Medicine (DICOM) data of subjects meeting the selection criteria of the study were manually segmented by two experienced neurosurgeons. The hematoma segmentation model was built based on watershed algorithm and regional growth algorithm. Seed point selected by neurosurgeons was taken as the starting point of growth. Regional grayscale difference criterion combined with manual segmentation validation were adopted to determine the regional growth threshold that met the segmentation precision requirements for intracranial hematoma. Using manual segmentation as the gold standard, Bland-Altman consistency analysis was used to verify the consistency of the three other methods for measuring hematoma volume.
With manual segmentation as the gold standard, among the three methods of measuring hematoma volume, algorithm segmentation had the smallest percentage error, the narrowest range of difference, the highest intra-group correlation coefficient (0.987), good consistency, and the narrowest 95% limits of agreement ( ). The percentage error of its segmentation was not statistically significant for hematomas of different volumes.
The segmentation method of spontaneous intracerebral hemorrhage based on watershed and regional growth algorithm shows stable measurement performance and good consistency with the clinical gold standard, which has considerable clinical significance, but it still needs further validation with more clinical samples.
建立一种基于分水岭和区域生长算法的脑血肿CT图像分割方法，以快速、准确地测量血肿体积，探讨该分割方法与临床金标准手动分割结果之间的一致性，并将该方法的结果与临床常用的两种田田公式计算结果进行比较。
回顾性收集2018年1月至2019年6月在四川大学华西医院神经外科接受自发性脑出血治疗的152例患者的术前CT图像。利用随机数字表将CT图像随机分为训练集、测试集和验证集，分别包含100例、22例和30例患者。训练集和测试集的标注结果用于算法训练和测试。对验证集采用手动分割、算法分割（即基于分水岭和区域生长算法的分割计算）、田田公式（即传统田田公式计算）和精确田田公式（即基于3D-Slicer的精确田田公式计算）四种方法测量血肿体积。由两名经验丰富的神经外科医生对符合研究入选标准的受试者的医学数字成像和通信（DICOM）数据进行手动分割。基于分水岭算法和区域生长算法建立血肿分割模型。以神经外科医生选择的种子点作为生长起点。采用区域灰度差异准则结合手动分割验证来确定满足颅内血肿分割精度要求的区域生长阈值。以手动分割为金标准，采用Bland-Altman一致性分析验证其他三种测量血肿体积方法的一致性。
以手动分割为金标准，在三种测量血肿体积的方法中，算法分割的百分比误差最小，差异范围最窄，组内相关系数最高（0.987），一致性良好，95%一致性界限最窄（ ）。其分割的百分比误差对于不同体积的血肿无统计学意义。
基于分水岭和区域生长算法的自发性脑出血分割方法显示出稳定的测量性能，与临床金标准具有良好的一致性，具有相当的临床意义，但仍需更多临床样本进一步验证。
