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

该项目旨在开发一种可解释的、医生在环的AI辅助软件，该软件可以在MRI中准确描绘胶质瘤边界，计算体积曲线，并在纵向研究中统计量化肿瘤生长。视觉分析和手动轮廓绘制的当前临床实践 肿瘤的诊断是主观的、耗时的，并且常常不一致。MRIMath的可解释，可信赖和医生在环AI系统的新奇是多方面的。首先，我们介绍了一个多尺度特征提取框架，该框架使用了U-Net图像收缩和扩展路径的初始模块 分段神经网络架构。其次，我们提出了一个新的损失函数的基础上修改的骰子相似系数。第三，我们使用两种学习机制来训练和测试AI系统：学习分割肿瘤内结构和学习分割胶质瘤子区域。最后，我们产生热量 地图来可视化人工智能提取的特征，从而为医生提供人工智能的注意力模式和在人工智能决策过程中触发的激活地图。直观的交互式用户界面将允许医生查看轮廓绘制结果，进行调整和批准轮廓， 可视化AI的解释和体积测量，最后查看统计分析的结果。医生所做的任何修改将在以后用于重新训练AI。

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.