Addressing algorithmic and data challenges to deep learning based segmentation of spine anatomy

项目总结/文摘

Project Summary/Abstract In spine medicine, subjective interpretation of biomedical images often leads to wrong diagnoses, prolonged non-surgical treatment for surgically treatable patients, and surgical treatment when none is necessary. Objective computerized analysis of the aforementioned images using deep learning has the potential to improve surgical outcomes while driving down the cost of surgery by eliminating unnecessary surgery and expediting necessary ones. Yet several barriers stymie the development and deployment of deep learning technology to operationalize imaging biomarker-based treatment recommendation in surgical practice. First, a publicly available database is absent to help train and validate algorithms for spinal pathologies. Second deep learning techniques remain difficult to train and operationalize in the clinical setting, due to various challenges. These include – 1. The lack of a framework to link generalization error to training data in deep learning-based segmentation, due to which performance estimates of algorithms are untenable prior to deployment 2. the lack of a disciplined approach to improve deep network performance on medical image segmentation and 3. the lack of frameworks that enable deep networks to identify and flag a difficult case and failed cases where a human expert should be consulted. First, we propose to develop a publicly accessible spine imaging database to promote the development of deep learning algorithms. Second, we aim to address the aforementioned technical challenges by 1. Developing a power-law scaling based framework to link training sample size and generalization error analytically 2. Proposing and validating a mathematical framework to create deep learning ensembles from deep learning models to guarantee improvement in segmentation performance 3. Developing and validating a Von-Neumann information- based score to endow deep learning ensembles with the ability to identify difficult cases and predict failure.