image segmentation — 2019

Team 7: Using Machine Learning to Improve Post-Acute Rehabilitation Process Flow

Develop a prediction model to streamline the transition of care process for hospital patients transitioning into rehabilitation facilities, with the goal of decreasing acute length of stay, facilitating outside referrals when needed and decreasing attrition from our health system.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerdeep learning, tumor classification, annotation, image segmentation, hackathon, u-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, bioinformatics, Satwik Rajaram

Team 8: Automated algorithm for detecting and localizing enteric tubes to facilitate accurate and timely radiographic interpretation

Create an image analysis tool to accurately localize enteric tubes and flag potential problems on radiographs in clinical practice.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerdeep learning, pathology, tumor classification, annotation, image segmentation, u-hack med, UT Southwestern Medical Center, hackathon, Lyda Hill Department of Bioinformatics, bioinformatics, gaudenz danuser, andrew jamieson

Team 9: Real-Time Cardiac Assessment of Catheterization-Derived Fick and CMR-Derived Flow

Improve workflow, reliability and reproduciblity of interventional cardiac MRI through a web/app-based user-friendly display for real-time assessment.

2019 TEAMS