A three-dimensional medical image segmentation app using graphic theory

We present a smartphone App for three-dimensional (3-D) medical image segmentation aimed at providing a portable image computing solution for physicians. Smartphones have become powerful portable devices to not only visualize images but also provide basic annotation tasks for diagnosis and reviewing treatment planning. In this paper, we present a fast region of interest (ROI) segmentation App for medical images. Using CT lung images, we show that such images can be streamed onto the smartphone from the picture archiving and communication system (PACS), and tumor or other ROIs are segmented by simply pinpointing initial points inside and outside an ROI on the image. Image visualization is also provided so that radiological report or surgical (e.g., intervention) planning can be accomplished through the interface. To ensure fast computation with the limited CPU memory, a graph-based segmentation algorithm is tailored to occupy less memory using a streaming-style computation. Once the image is loaded, a graph is computed to reflect the spatial-intensity relationship among the voxels. Then, given initial points inside and outside an ROI, segmentation is performed based on the graph. Quantitative results show that fast and accurate segmentation can be achieved using the proposed method. We expect that such Apps can be utilized for image reading, ROI annotation, or surgical planning review in the areas of radiology, intervention, and oncology.