MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN

Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor se...

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Bibliographic Details
Published in:Computer engineering and applications journal 2024-10, Vol.13 (3), p.1-9
Main Author: Nasrudin, Muhammad
Format: Article
Language:English
Subjects:
Brain
Image analysis
Image segmentation
Instance segmentation
Magnetic resonance imaging
Medical imaging
Tumors
Online Access:Get full text
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Summary:Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean Average Precision (mAP) metric. The results demonstrate that our model achieves a high mAP of 90.3%, indicating its effectiveness in accurately segmenting brain tumors. This automated approach not only reduces the manual effort required for tumor segmentation but also provides consistent and reliable results, potentially improving clinical outcomes.
ISSN:2252-4274
2252-5459
DOI:10.18495/comengapp.v13i03.490