Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model. (July 2022)

• Record Type: Journal Article
• Authors: Inkeaw, Papangkorn; Angkurawaranon, Salita; Khumrin, Piyapong; Inmutto, Nakarin; Traisathit, Patrinee; Chaijaruwanich, Jeerayut; Angkurawaranon, Chaisiri; Chitapanarux, Imjai
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: Traumatic brain injury -- Intracranial hemorrhage -- Segmentation -- Deep learning -- Computerized tomography scan; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105530
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of intracranial hemorrhage that may necessitate surgical intervention include subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage. We proposed a novel automatic method for segmenting the hemorrhage subtypes on a CT scan by integrated CT scan with bone window as input of a deep learning model. Brain CT scans were collected from adult patients and annotated regions of subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage by neuroradiologists. Their raw DICOM images were preprocessed by two different window settings i.e., subdural and bone windows. The collected CT scans were divided into two datasets namely training and test datasets. A deep-learning model was modified to segment regions of each hemorrhage subtype. The model is a three-dimensional convolutional neural network including four parallel pathways that process the input at different resolutions. It was trained by a training dataset. After the segmentation result was produced by the deep-learning model, it was then improved in the post-processing step. The size of the segmented lesion was considered, and a region-growing algorithm was applied. We evaluated the performance of the proposed method on the test dataset. The method reached the median Dice similarity coefficients higher than 0.37 for each hemorrhage subtype. The proposed method demonstrates higher Dice similarity coefficients and improved segmentation performance compared to previously published literature. Highlights: Automatic segmentation method for separate three intracranial hemorrhage subtypes. Integrating CT scan with bone window to input of a deep learning segmentation model. Evaluation and discussion in statistical and radiological viewpoints.