Agreement evaluation of an automatic segmentation algorithm for quantifying subdural/epidural hemorrhage volume using convolution neural network

Abstract

Objective To validate the agreement among the convolution neural network segmentation algorithm, Tada formula and manual segmentation for subdural/epidural hemorrhage volume. Methods A total of 129 cases with 352 subdural/epidural hemorrhage CT scans were extracted from Chinese Intracranial Hemorrhage Image Database (CICHID) from January 2017 to June 2019. All CT scans were measured by three methods including manual segmentation, algorithm segmentation and Tada formula. The manual segmentation was regarded as the "golden standard" and the agreement test among three methods was performed. We explored the influence factors in different measurement methods, such as the shape or boundary of hematoma. Results Compared with the Tada formula method, the percentage error of segmentation algorithm was small (23.62%), and the agreement between algorithm and the manual reference was strong, which 94.89% (334/352) of the data was within the 95% limits of agreement (95%LoA), however, the 95%LoA was broad. And the performance of segmentation algorithm showed better in asymmetry (P = 0.000) and clear boundary hematoma (P = 0.000). Conclusions The segmentation algorithm based on convolution neural network has a certain application prospect, but need to be validated in large sample research. doi：10.3969/j.issn.1672⁃6731.2021.03.011

Keywords

• hematoma
• subdural
• epidural
• cranial
• artificial intelligence
• neural networks (computer)
• tomography
• x⁃ray computed