A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images

Muntakim Mahmud Khan; Muhammad E H Chowdhury; A S M Shamsul Arefin; Kanchon Kanti Podder; Md Sakib Abrar Hossain; Abdulrahman Alqahtani; M Murugappan; Amith Khandakar; Adam Mushtak; Md Nahiduzzaman

doi:10.3390/diagnostics13152537

A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images

Diagnostics (Basel). 2023 Jul 31;13(15):2537. doi: 10.3390/diagnostics13152537.

Authors

Affiliations

¹ Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.
² Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
³ Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City 11952, Saudi Arabia.
⁴ Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
⁵ Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait.
⁶ Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India.
⁷ Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Perlis 02600, Malaysia.
⁸ Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar.
⁹ Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Abstract

Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.

Keywords: Dice similarity coefficient (DSC); computed tomography; convolution neural network; deep learning; intersection over union (IoU); intracranial hemorrhage.

Abstract

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