- Overview
This project focuses on diagnosing Alzheimer’s disease using advanced segmentation of brain MRI images. The system predicts disease severity based on patients' age and brain atrophy levels, utilizing quantitative radiology and AI-based image analysis for improved accuracy.
- Significance of the Project
Global Impact: Alzheimer’s disease currently affects 48 million people worldwide, a figure projected to double to 90 million by 2050, placing immense pressure on healthcare systems.
Diagnosis Challenges: Traditional diagnosis methods rely heavily on visual analysis by radiologists, which can be subjective and inconsistent.
AI-Driven Solution: Automated segmentation and volumetry provide objective, quantifiable insights, reducing inter-radiologist variability.Key Features and Workflow
Quantitative Volumetry Algorithms: Segments and quantifies brain structures such as hippocampus, thalamus, amygdala, brainstem, white matter, gray matter, and cerebrospinal fluid (CSF).Generates accurate volume metrics and age-normalized percentile tables.
- Volumetry Implementation:
Converts DICOM T1-weighted images (.dcm) to NIfTI format (.nii) for preprocessing.
Removes non-brain tissues (e.g., skull, eyes) to isolate brain structures.
Classifies brain tissues into white matter, gray matter, and CSF.
Extracts and quantifies subcortical structures.
- Output:
A comprehensive PDF report summarizing volumetric data and disease progression.Age-normalized tables highlighting deviations from normal ranges.Tissue maps and structural volumes for detailed visual analysis.Modules and Algorithms
- MRI Analysis:
Segmentation of brain regions using advanced FSL libraries.Quantification of volumetric changes related to Alzheimer’s progression.
- Machine Learning Models:
Automated classification of disease severity using volumetric data.Integration of deep learning algorithms for high-precision tissue mapping.
- Quantitative Radiology Features:
Identifies white matter hyperintensities and cortical atrophy using Fazekas scale and MTA criteria.Facilitates personalized radiology reports with actionable insights.
- Advantages of the System
Accuracy and Precision: AI tools provide more reliable measurements than manual methods, leading to earlier and more accurate diagnoses.
Consistency: Reduces variability between radiologists by standardizing the diagnostic process.
Efficiency: Automates labor-intensive tasks, enabling rapid and detailed image analysis
- Databases Utilized
ADNI (Alzheimer’s Disease Neuroimaging Initiative): Includes ADNI-1, ADNI-Go, ADNI-2, and ADNI-3 for multimodal imaging and biomarker data
- Future Potential
Integration of longitudinal data to track disease progression.Expansion of the model for multi-disease diagnosis using similar neuroimaging techniques.This AI-powered diagnostic model represents a significant leap forward in the early detection and management of Alzheimer’s disease, paving the way for more personalized and effective treatment strategies.