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Towards a Digital Twin in Human Brain: Brain Tumor Detection Using K-Means
Anastasios Loukas Sarris1, Efstathios Sidiropoulos1, Evangelos Paraskevopoulos2
1Anaptixiaki Meletitiki Voriou Ellados.
Studies in Health Technology and Informatics
|May 19, 2023
Summary
This study introduces a K-means algorithm for brain tumor detection from MRI scans, creating a 3D model for a digital twin. This advances personalized medicine and patient treatment prediction in healthcare.
Area of Science:
- Medical Imaging
- Computational Biology
- Digital Health
Background:
- Digital Twins offer transformative potential in healthcare by simulating and predicting patient diagnosis and treatment.
- Accurate patient-specific models are crucial for advancing personalized medicine.
Purpose of the Study:
- To present a K-means based algorithm for brain tumor detection using MRI scans.
- To design a 3D model for the digital twin of a patient's brain.
Main Methods:
- Utilized K-means clustering algorithm for image segmentation and feature extraction from MRI scans.
- Developed a 3D modeling approach to reconstruct the brain tumor and surrounding structures.
Main Results:
- Successfully detected brain tumors from MRI data using the K-means algorithm.
- Generated a detailed 3D model representing the detected tumor and anatomical context.
Conclusions:
- The proposed algorithm and 3D modeling are foundational steps towards creating patient-specific digital twins in neuro-oncology.
- This approach facilitates enhanced visualization and aids in predicting diagnosis and treatment outcomes.
