Collaborative Research: SI2-SSE: High-Performance Workflow Primitives for Image Registration and Segmentation

图像配准和分割是解决许多复杂的数据驱动问题的重要技术。例子包括个体化医疗，其中通过分析随时间推移的MRI、CT或超声图像来监测疾病进展;识别医学图像中的解剖结构;识别视频片段中的物体和人;以及提取可成像的生物特征，如指纹、面部和虹膜。现在可以轻松获取图像和视频，其速度远远超过我们执行高级图像分析的能力。 出于这个原因，高级配准和分割算法通常不用于许多大规模和时间敏感的应用，因为它们需要比可用的更多的处理时间。该项目将通过开发一个高性能的图像配准和分割软件包来弥补这种情况，该软件包适合在大规模并行处理器上运行，并围绕它建立强大的用户和开发人员基础。通过该项目开发的所有软件都将是开源的，并根据MIT许可证进行许可。该平台所实现的处理速度的提高将对计算机视觉、数字取证和生物医学图像分析等学科产生重大影响。最后，项目团队致力于德雷克塞尔大学的多样性使命，并将在为该项目招募研究生时接触代表性不足的群体。选定的研究任务将被整合到现有的课程和课程将开发新的经验程序源于这一努力。该项目的总体目标是开发一个高性能，众核CPU和GPU加速算法软件包的攻击类的问题，依赖于解决方案的数据密集的逆问题，如注册，分割，断层扫描，和参数估计。具体的技术方法涉及开发广泛的基于推理和分析的工作流所需的算法原语。概率原语建立生成，歧视，和条件随机场分类模型将实施重点对象分割。 将为样条和体素驱动算法开发专门的配准运算符。这些原语将在单指令多数据范式中开发，该范式通过OpenMP，CUDA和OpenCL利用众核处理架构。工作流程将由图形用户界面（GUI）补充，提供功能丰富的工具工作室，以可视化和直观的方式向科学家展示高性能的原语。平台架构将被设计为分布式系统服务，目标是本地管理的科学计算集群，其中计算节点的数量将能够根据负载要求进行扩展。GUI和计算核心可以在分布式客户端-服务器配置中运行，或者一起在单个高性能工作站上本地运行。重点是采用所需的文档和视频/书面教程。项目团队将使用开放的软件开发模型来建立强大的用户群，包括新手用户和需要在稳定的软件基础设施上实现新算法的研究人员。预计该工具及其源代码的可用性将促进跨研究学科的定量图像分析的增加。

Image registration and segmentation are vital enabling technologies for addressing many complex, data driven problems. Examples include individualized medical treatment where disease progression is monitored by analyzing MRI, CT, or ultrasound images over time; identifying anatomical structures in medical images; recognizing objects and people in video footage; and extracting imageable biometrics such as fingerprints, faces, and the iris. Images and videos can now be easily acquired at a rate that far surpasses our capacity to perform advanced image analysis. For this reason, advanced registration and segmentation algorithms are not routinely used for many large-scale and time sensitive applications because they require more processing time than is available. This project will remedy this situation by developing a high-performance software package for image registration and segmentation, suitable to be run on massively parallel processors, and building a strong user and developer base around it. All software developed through the project will be open source and licensed under the MIT License. Improvements in processing speed achieved by the proposed platform will have significant impact in disciplines such as computer vision, digital forensics, and biomedical image analysis. Finally, the project team is committed to the diversity mission of Drexel University and will reach out to under-represented groups when recruiting graduate students for this project. Selected research tasks will be integrated within existing courses and curriculum will be developed for new experiential programs stemming from this effort.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.