High-Performance Workflow Primitives for Image Registration and Segmentation

The overall goal of this project is to develop a high-performance, many-core CPU and GPU accelerated algorithmic software package for attacking classes of problems that depend on solutions to data-dense inverse problems such as registration, segmentation, tomography, and parameter estimation. The specific technical approach involves developing algorithmic primitives required by a broad class of inference and analysis based workflows. Probabilistic primitives for building generative, discriminative, and conditional random field classification models will be implemented with emphasis on object segmentation. Specialized registration operators will be developed for spline and voxel-driven algorithms. These primitives will be developed within the single instruction multiple data paradigm which utilizes many-core processing architectures via OpenMP, CUDA, and OpenCL. The workflow will be supplemented by a graphical user interface (GUI), providing a feature rich studio of tools that expose high-performance primitives to scientists visually and intuitively. The platform architecture will be designed as a distributed system service targeting locally administered scientific computing clusters where the number of compute nodes will be able to scale with load requirements. The GUI and the computational core may either run in a distributed client-server configuration or together locally on a single high performance workstation. Emphasis will be placed on documentation and video/written tutorials necessary for adoption. The project team will use an open software development model to build a strong user base comprising both novice users as well as researchers with the need to implement new algorithms on top of a stable software infrastructure. It is expected that the availability of this tool and its source code will catalyze an increase in quantitative image analysis spanning across research disciplines.