An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by...

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Bibliographic Details
Published in:Journal of imaging 2024-04, Vol.10 (4), p.77
Main Authors: Hoang, Quoc Tuan, Pham, Xuan Hien, Trinh, Xuan Thang, Le, Anh Vu, Bui, Minh V, Bui, Trung Thanh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Blood vessels
Brain
Clinical outcomes
Comparative analysis
Computed tomography
computer-aided diagnosis
Computer-aided medical diagnosis
convolutional network
CT imaging
CT scans
Data augmentation
Datasets
Deep learning
Edema
Emergency medical care
Epidural
Head injuries
Hematoma
Hemorrhage
Hypertension
Image segmentation
intracranial hemorrhage
Ischemia
Machine learning
Magnetic resonance imaging
Medical imaging
Medical personnel
Methods
Neural networks
Patients
Performance evaluation
Technology application
Tomography
Trauma
Traumatic brain injury
Ultrasonic imaging
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Summary:Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging10040077