SBIR Phase I Topic 402 - Artificial Intelligence-Aided Imaging for Cancer Prevention, Diagnosis, and Monitoring

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This project aims to develop an interpretable, physician-in-the-loop AI-aided software that accurately delineates glioma boundaries in MRIs, computes volumetric curves, and statistically quantifies the tumor growth in longitudinal studies. The current clinical practice of visually analyzing and manually contouring tumors is subjective, time-consuming, and often inconsistent. The novelty of MRIMath's explainable, trustworthy, and physician-in-the-loop AI system is multi-fold. First, we introduce a multi-scale feature extraction framework using the inception modules in contracting and expanding paths of the U-Net image segmentation neural network architecture. Second, we propose a new loss function based on the modified Dice similarity coefficient. Third, we train and test the AI system using two learning regimes: learning to segment intra-tumoral structures and learning to segment glioma sub-regions. Finally, we produce heat maps to visualize the features extracted by the AI, thus offering physicians a view of AI's attention patterns and activation maps that were triggered during AI's decision-making. An intuitive and interactive User Interface will allow the physician to review contouring results, make adjustments and approve contours, visualize AI's explanations and volumetric measurements, and finally review the results of the statistical analysis. Any modifications made by the physician will be used later to re-train AI.