Gremse Felix, Stärk Marius, Ehling Josef, Menzel Jan Robert, Lammers Twan, Kiessling Fabian
1. Experimental Molecular Imaging, University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
2. Computer Graphics and Multimedia, RWTH Aachen University, Aachen, Germany.
Theranostics. 2016 Jan 1;6(3):328-41. doi: 10.7150/thno.13624. eCollection 2016.
A software tool is presented for interactive segmentation of volumetric medical data sets. To allow interactive processing of large data sets, segmentation operations, and rendering are GPU-accelerated. Special adjustments are provided to overcome GPU-imposed constraints such as limited memory and host-device bandwidth. A general and efficient undo/redo mechanism is implemented using GPU-accelerated compression of the multiclass segmentation state. A broadly applicable set of interactive segmentation operations is provided which can be combined to solve the quantification task of many types of imaging studies. A fully GPU-accelerated ray casting method for multiclass segmentation rendering is implemented which is well-balanced with respect to delay, frame rate, worst-case memory consumption, scalability, and image quality. Performance of segmentation operations and rendering are measured using high-resolution example data sets showing that GPU-acceleration greatly improves the performance. Compared to a reference marching cubes implementation, the rendering was found to be superior with respect to rendering delay and worst-case memory consumption while providing sufficiently high frame rates for interactive visualization and comparable image quality. The fast interactive segmentation operations and the accurate rendering make our tool particularly suitable for efficient analysis of multimodal image data sets which arise in large amounts in preclinical imaging studies.
本文介绍了一种用于医学体数据集交互式分割的软件工具。为实现对大数据集的交互式处理，分割操作和渲染均采用GPU加速。针对GPU带来的限制，如内存有限和主机-设备带宽受限等问题，进行了特殊调整。通过对多类分割状态进行GPU加速压缩，实现了通用且高效的撤销/重做机制。提供了一套广泛适用的交互式分割操作，这些操作可组合起来解决多种类型成像研究的量化任务。实现了一种用于多类分割渲染的全GPU加速光线投射方法，该方法在延迟、帧率、最坏情况内存消耗、可扩展性和图像质量方面达到了良好的平衡。使用高分辨率示例数据集对分割操作和渲染的性能进行了测量，结果表明GPU加速大大提高了性能。与参考的移动立方体实现相比，该渲染在渲染延迟和最坏情况内存消耗方面表现更优，同时为交互式可视化提供了足够高的帧率以及可比的图像质量。快速的交互式分割操作和精确的渲染使得我们的工具特别适用于高效分析临床前成像研究中大量出现的多模态图像数据集。
