ArticleDiagnostics (Basel, Switzerland)2023
A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 18 citations in OpenAlex.
- Atherosclerosis diagnosis from magnetic resonance images: an empirical approach to automate carotid vessel wall segmentation and feature-based prediction.Physical and engineering sciences in medicine · 2026Article
- Review
- Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment using Classification and Segmentation.Journal of imaging informatics in medicine · 2025Article
- A deep learning approach to predict temporal changes of subdural hemorrhage on computed tomography.Scientific reports · 2025Article
- A novel multimodal computer-aided diagnostic model for pulmonary embolism based on hybrid transformer-CNN and tabular transformer.Physical and engineering sciences in medicine · 2025Article
- Utilizing shallow features and spatial context for weakly supervised intracerebral hemorrhage segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning Framework for Liver Segmentation fromSensors (Basel, Switzerland) · 2023Article
- Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
10 authors at 6 institutions in 6 countries.
Funding
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.
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