Interactive Segmentation Quality Evaluator
Educational tool for understanding segmentation metrics through hands-on boundary drawing and evaluation
Overview
An interactive MATLAB application that teaches image segmentation evaluation metrics through hands-on boundary drawing and real-time quality assessment against ground truth references.
Educational Focus: Image Segmentation, Quality Metrics, Computer Vision Fundamentals
Educational Motivation
Image segmentation is fundamental to computer vision and medical imaging, but understanding quality metrics can be abstract without hands-on experience. Students struggle to understand Dice coefficient, Hausdorff distance, and pixel accuracy conceptually, often facing a practical disconnect between mathematical formulas and real-world application. There’s also a need to understand expert variability and inter-observer agreement in segmentation quality, as well as the clinical relevance connecting segmentation accuracy to downstream applications.
Key Features
Interactive Drawing Interface
The system provides real-time segmentation capability allowing users to draw boundaries directly on target images, with support for multiple objects enabling practice with various anatomical structures and objects. Users receive instant feedback as they see quality metrics update while drawing, with ground truth comparison through visual overlay of reference segmentations.
Comprehensive Metrics
The tool implements key evaluation measures including the Dice Score Coefficient for overlap-based similarity measurement, Hausdorff Distance for maximum distance between boundary points, and Pixel Accuracy for point-wise classification correctness. Visual feedback is provided through color-coded overlays showing agreement and disagreement areas.
Educational Scaffolding
The learning experience incorporates progressive difficulty starting with simple shapes and advancing to complex anatomical structures. Students receive immediate assessment through real-time feedback on segmentation quality and can make multiple attempts to practice and improve segmentation accuracy. The tool also supports comparative analysis allowing users to see how different drawing approaches affect metrics.
Technical Implementation
Platform: MATLAB with interactive UI components
Input Methods: Mouse-based boundary drawing with curve smoothing
Metric Calculations: Real-time computation of standard segmentation quality measures
Visualization: Multi-color overlays for intuitive quality assessment
Learning Outcomes
Students using this tool will master segmentation metrics by understanding how different quality measures respond to boundary variations, develop practical skills through experience in manual segmentation techniques, appreciate quality trade-offs by seeing how speed versus accuracy affects different metrics, and connect theory to practice by bridging mathematical concepts with visual understanding.
Applications in Education
Computer Vision Courses
The tool supports fundamental concepts through introduction to segmentation and evaluation metrics, provides practical exercises with hands-on experience in real evaluation scenarios, and enables metric comparison to understand when different quality measures are appropriate.
Medical Imaging Training
In medical contexts, the tool provides clinical relevance through practice with anatomical structures and medical images, helps students understand quality standards and acceptable segmentation quality for clinical applications, and offers inter-observer studies to experience the challenge of consistent manual segmentation.
Deep Learning Education
For deep learning applications, the tool provides loss function understanding by showing how segmentation metrics translate to training objectives, supports performance evaluation through learning standard evaluation protocols for segmentation models, and develops quality assessment skills with intuition for model performance evaluation.
Impact on Understanding
This interactive approach transforms abstract mathematical concepts into tangible, visual experiences through immediate feedback where students see the impact of their decisions in real-time. The experiential learning approach allows understanding to develop through practice rather than memorization, building metric intuition as visual feedback helps students understand what metrics actually measure while developing practical segmentation and evaluation capabilities.
Repository & Resources
Complete source code, sample images, and educational materials available on GitHub and MATLAB File Exchange, enabling widespread adoption in educational settings.