Theranostics 2016; 6(3):328-341. doi:10.7150/thno.13624

Research Paper

Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data

Felix Gremse1, Marius Stärk1, Josef Ehling1, Jan Robert Menzel2, Twan Lammers1, Fabian Kiessling1✉

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

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) License. See for full terms and conditions.
Gremse F, Stärk M, Ehling J, Menzel JR, Lammers T, Kiessling F. Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data. Theranostics 2016; 6(3):328-341. doi:10.7150/thno.13624. Available from

File import instruction


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.

Keywords: Interactive Segmentation, Medical Image Analysis, Multimodal Imaging, GPU Processing, Segmentation Rendering, Undo/Redo