Brain Tumor Detection Using MRI Segmentation
- Technology: Artificial Intelligence, Deep Learning, Image Processing & Computer Vision
- Industry: Healthcare
Business Objectives
Medical imaging is pivotal in diagnosing and planning treatment for brain tumors, impacting millions of people globally.
Accurate segmentation and classification of tumors from MRI scans are critical to help clinicians delineate boundaries, quantify tumor burden, and identify subtypes.
Manual segmentation is time-consuming and inconsistent. Automated methods can save time and improve accuracy, directly supporting better clinical decision-making.
Solution
We leveraged the Medical Decathlon dataset, a benchmark in medical image analysis. It includes multi-modal MRI scans across different tumor types—gliomas, meningiomas, and pituitary tumors—with expert pixel-level annotations for tumor regions.
The dataset, enriched with patient metadata and annotation reviews, enabled consistent evaluation, meaningful patient-tumor insights, and robust preprocessing for training.
To build the model, we applied the UNET architecture, a U-shaped encoder-decoder network proven effective in medical imaging. The encoder extracts high-level features, while the decoder reconstructs them into a pixel-wise segmentation map.
Skip connections bridge encoder and decoder layers, preserving spatial detail and improving localization. This design ensures both global tumor context and fine-grained boundaries are captured.
Benefits
The model was evaluated using standard metrics such as Dice coefficient and Intersection over Union (IoU). Results demonstrated a high overlap between predicted tumor regions and expert annotations.
The UNET architecture showed strong reliability in identifying tumor boundaries, subtypes, and tumor burden from MRI scans. Its ability to combine feature extraction, global context, and local detail led to precise and detailed segmentation results.
For clinicians, this translates to faster and more accurate tumor delineation, improved diagnosis, and better treatment planning.
Beyond reducing manual effort, the system holds significant potential for improving clinical decision-making and enhancing patient outcomes in real-world healthcare settings.
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