Radford Juang

Patients at a disaster scene can greatly benefit from technologies that continuously monitor their vital status and track their locations until they are admitted to the hospital. We have designed and developed a real-time patient monitoring system that integrates vital signs sensors, location sensors, ad-hoc networking, electronic patient records, and web portal technology to allow remote monitoring of patient status. This system shall facilitate communication between providers at the disaster scene, medical professionals at local hospitals, and specialists available for consultation from distant facilities.

We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn. We describe a spawning model for the GM-PHD filter as well as modifications to the original GM-PHD algorithm to track lineage. Experimental results are provided illustrating the approach for dense cell colonies.

In this paper, we propose a graph-based method for fully-automatic segmentation of the left ventricle and atrium in 3D ultrasound (3DUS) volumes. Our method requires no user input and can segment volumes with open and closed mitral valves. We utilize the radial symmetry transform to determine a central axis along which the 3D volume is warped into a cylindrical coordinate space. A graph is constructed for the volume in this space and a min-cut algorithm is applied to segment the left ventricle and atrium from the background. Since segmentation in the cylindrical coordinate space is defined as finding a boundary between the left (interior) and right (exterior) sides, we obviate the need for user specified seeds. The segmented results are transformed back to the Cartesian coordinate space. Experiments using intraoperative 3D ultrasound data show promising results.