Development of advanced algorithms for large datasets
Schmelzle S., Heethoff M., Heuveline V., Losel P., Becker J., Beckmann F., Schluenzen F., Hammel J.U., Kopmann A., Mexner W., Vogelgesang M., Jerome N.T., Betz O., Beutel R., Wipfler B., Blanke A., Harzsch S., Hornig M., Baumbach T., Van De Kamp T.
in Proceedings of SPIE – The International Society for Optical Engineering, 10391 (2017), 103910P. DOI:10.1117/12.2275959
© 2017 SPIE. Beamtime and resulting SRμCT data are a valuable resource for researchers of a broad scientific community in life sciences. Most research groups, however, are only interested in a specific organ and use only a fraction of their data. The rest of the data usually remains untapped. By using a new collaborative approach, the NOVA project (Network for Online Visualization and synergistic Analysis of tomographic data) aims to demonstrate, that more efficient use of the valuable beam time is possible by coordinated research on different organ systems. The biological partners in the project cover different scientific aspects and thus serve as model community for the collaborative approach. As proof of principle, different aspects of insect head morphology will be investigated (e.g., biomechanics of the mouthparts, and neurobiology with the topology of sensory areas). This effort is accomplished by development of advanced analysis tools for the ever-increasing quantity of tomographic datasets. In the preceding project ASTOR, we already successfully demonstrated considerable progress in semi-automatic segmentation and classification of internal structures. Further improvement of these methods is essential for an efficient use of beam time and will be refined in the current NOVAproject. Significant enhancements are also planned at PETRA III beamline p05 to provide all possible contrast modalities in x-ray imaging optimized to biological samples, on the reconstruction algorithms, and the tools for subsequent analyses and management of the data. All improvements made on key technologies within this project will in the long-term be equally beneficial for all users of tomography instrumentations.
Losel P., Heuveline V.
in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10129 LNCS (2017) 121-128. DOI:10.1007/978-3-319-52280-7_12
© Springer International Publishing AG 2017. Segmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance (CMR) image allows to create a patient-specific heart model for surgical planning in children with complex congenital heart disease (CHD). Implementation of semi-automatic or automatic segmentation algorithms is challenging because of a high anatomical variability of the heart defects, low contrast, and intensity variations in the images. Therefore, manual segmentation is the gold standard but it is labor-intensive. In this paper we report the set-up and results of a highly scalable semi-automatic diffusion algorithm for image segmentation. The method extrapolates the information from a small number of expert manually labeled reference slices to the remaining volume. While results of most semi-automatic algorithms strongly depend on well-chosen but usually unknown parameters this approach is parameter-free. Validation is performed on twenty 3D CMR images.
Losel P., Heuveline V.
in Progress in Biomedical Optics and Imaging – Proceedings of SPIE, 9784 (2016), 97842L. DOI:10.1117/12.2216202
© 2016 SPIE. Inspired by the diffusion of a particle, we present a novel approach for performing a semiautomatic segmentation of tomographic images in 3D, 4D or higher dimensions to meet the requirements of high-throughput measurements in a synchrotron X-ray microtomograph. Given a small number of 2D-slices with at least two manually labeled segments, one can either analytically determine the probability that an intelligently weighted random walk starting at one labeled pixel will be at a certain time at a specific position in the dataset or determine the probability approximately by performing several random walks. While the weights of a random walk take into account local information at the starting point, the random walk itself can be in any dimension. Starting a great number of random walks in each labeled pixel, a voxel in the dataset will be hit by several random walks over time. Hence, the image can be segmented by assigning each voxel to the label where the random walks most likely started from. Due to the high scalability of random walks, this approach is suitable for high throughput measurements. Additionally, we describe an interactively adjusted active contours slice by slice method considering local information, where we start with one manually labeled slice and move forward in any direction. This approach is superior with respect to accuracy towards the diffusion algorithm but inferior in the amount of tedious manual processing steps. The methods were applied on 3D and 4D datasets and evaluated by means of manually labeled images obtained in a realistic scenario with biologists.