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An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging
Quoc Tuan Hoang1, Xuan Hien Pham2, Xuan Thang Trinh1
1Faculty of Mechanical Engineering, Hung Yen University of Technology and Education, 39Rd., Hung Yen 160000, Vietnam.
Insights
This study introduces an improved computer-aided diagnosis method for detecting intracranial hemorrhage (ICH) in CT scans. The new technique enhances lesion localization and segmentation, aiding in faster and more accurate diagnosis of traumatic brain injury.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Neurosurgery
Background:
- Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) is a critical medical emergency requiring prompt diagnosis.
- Current diagnosis relies on expert interpretation of Computed Tomography (CT) scans, which can be subject to human error.
- Computer-aided diagnosis (CAD) systems offer potential to improve accuracy and efficiency in detecting ICH.
Purpose of the Study:
- To develop and validate a novel method for enhanced localization and segmentation of ICH lesions in CT scans.
- To improve the accuracy and reliability of computer-aided diagnosis for traumatic brain injury-related intracranial hemorrhage.
- To leverage data augmentation and deep learning for better ICH detection.
Main Methods:
- A U-Net-based segmentation network was employed for lesion segmentation.
- Multiple augmented images generated through various data augmentation techniques were utilized.
- Residual connections were integrated into the U-Net architecture to enhance training efficiency.
Main Results:
- The proposed method achieved a significant Intersection over Union (IOU) score of 0.807 ± 0.03 for ICH segmentation.
- Experiments were conducted on 82 CT scans from patients with traumatic brain injury.
- A 10-fold cross-validation strategy was used to rigorously evaluate the model's performance.
Conclusions:
- The novel data augmentation and U-Net with residual connections approach effectively enhances ICH lesion localization and segmentation.
- This method shows promise for improving the accuracy of computer-aided diagnosis systems for intracranial hemorrhage in TBI patients.
- The findings suggest a valuable tool for assisting radiologists and improving patient outcomes.
Abstract:
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.
