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[Hematoma Segmentation of Spontaneous Intracerebral Hemorrhage Based on Watershed and Region-Growing Algorithm].
Zhao, Jie-Yi; Zhou, Zheng-Song; Wang, Xiao-Yu; Zhang, Hao-Yu; Duan, Zong-Hao; Wang, Shun-Min; Wan, Hong-Li; Zhang, Tao.
Afiliación
  • Zhao JY; Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhou ZS; Chengdu Jincheng College, Chengdu 611731, China.
  • Wang XY; Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhang HY; Chengdu Jincheng College, Chengdu 611731, China.
  • Duan ZH; West China School of Medicine, Sichuan University, Chengdu 610041, China.
  • Wang SM; West China School of Medicine, Sichuan University, Chengdu 610041, China.
  • Wan HL; West China-PUMC C.C.Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
  • Zhang T; West China-PUMC C.C.Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 53(3): 511-516, 2022 May.
Article en Zh | MEDLINE | ID: mdl-35642163
Objective: 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. Methods: 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. Results: 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 ( LoA). The percentage error of its segmentation was not statistically significant for hematomas of different volumes. Conclusion: 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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hematoma Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: Zh Revista: Sichuan Da Xue Xue Bao Yi Xue Ban Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Hematoma Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: Zh Revista: Sichuan Da Xue Xue Bao Yi Xue Ban Año: 2022 Tipo del documento: Article País de afiliación: China