Highthroughput imaging computation morphometrics and visualization for morphological

High-throughput imaging, computation, morphometrics, and visualization for morphological phenomics Keith Cheng 1, Xuying Xin 1, Stephen Peckins 1, Jean Copper 1, Darin Clark 1, Donald Bigler 2, Rajkumar Kettimuthu 3, Xianghui Xiao 4, Francesco De Carlo 4, Patrick La Riviere 5, Gordon Kindlmann 3, 6, Jonathan Silverstein 3, 5, 6, and Ian Foster 3, 6 1 Jake Gittlen Cancer Research Foundation, Division of Experimental Pathology, 2 Dept of Radiology, Penn State Hershey College of Medicine, 3 Mathematics & Computer Science Institute, 4 Advanced Photon Source Argonne National Lab, 5 Dept of Radiology, 6 Dept of Computer Science, U Chicago Abstract The length scale of the zebrafish makes it ideal for whole -body characterization of cellular phenotypes. 3 D micronscale imaging will be necessary, but light-based imaging is limited by pigmentation and tissue thickness. Micronscale computed tomography using high-energy synchrotron-based X-rays is unaffected by those limitations, and in combination with tissue staining, yields images of unprecedented range of scale, from single cell to entire animal. The large file-sizes present conquerable challenges to reconstruction, segmentation, morphometrics, and visualization, and can become a key component of the zebrafish genetic phenome project. Such imaging can be readily extended to fish affected by diseases or chemicals, and to tissues of other model systems, including humans. High-throughput For every 10, 000 mutations (requiring multiple individual scans/mutation), current rates of imaging (20 minutes per scan comprised of 1504 separate rotational images) will take ~200 years. With newer imaging chips, scan times may be reduced to 1 minute, and potentially, 10 seconds. To make the phenome project feasible, sample preparation, loading, imaging, unloading, followed by file transfer, image reconstruction, segmentation, measurement, visualization, and web-accessibility will need to be automated and occur in real time, necessitating Improvements in engineering, imaging, segmentation software development, GPU-assisted GRID supercomputing, web-based interface toolbuilding, and reiterative testing with. Teams are being built and we invite partnerships with individual investigators, research communities and government agencies. Relevant numbers • 32 GB/scan (raw file), 1 -5 scans/fish • Tomographic reconstruction=> • 2048 x 2048 volume/scan • 32 -bit floating point • One folder of processed output ~24 GB/scan • For backup and volume analysis, need transfer to Penn State of both raw and processed files (56 GB/scan) • One computed fish volume 20 -100 GB • 1 year goal 1 scan/min => 32 GB x 60 min/hr = 2020 GB/hour, or ~2 TB/hour of raw files/scan, from which we derive 60 x 24 = 1. 44 TB/hr of processed files, from which we generate an unknown number of derived files; transfer speed needed 3. 4 TB/hour = 8. 3 gbit/sec • 3 year goal 1 scan/10 seconds => ~12 TB/hour of raw files/scan, 8. 64 TB/hour of processed data, totaling 20. 64 TB/hour = 50. 43 gbit/sec (6 x 10 gbit/sec lines) • We have achieved 2 TB/2 hour transfer rates; faster rates are anticipated Conclusions Results Third-generation high-energy synchrotron X-ray sources are required for generating 3 D images of whole zebrafish using micro. CT at cell resolutions, and to achieve scanning throughput required for the phenome project. Our voxel sizes have reached 1. 43 μ for juveniles and 0. 743μ for larvae. We are working towards improvements in scanning, data transfer speeds, networking, software development, ontological definition, database construction, bioinformatic integration with other model systems, and web-interface development. New Mac laptop PADS Beagle CPU 2. 8 -GHz 64 -bit Intel Core i 7 2. 66 -GHz 64 -bit Intel Nehalem 2. 1 -GHz 64 -bit AMD Opteron “Magny Cours” Cores/node 2 8 24 Memory/node 8 GB 24 GB 32 GB 17. 1 GB/s 25 GB/s 85. 3 GB/s #nodes 1 48 744 #cores 2 384 17856 Peak performance (TFLOPS) 0. 0224 TFLOPs 4. 25 TFLOPs 151 TFLOPs Total memory 8 GB 1. 1 TB 23. 3 TB Node disk 512 GB n/a Shared disk n/a 350 TB 640 TB Interconnect (MPI performance) n/a DDR Infiniband (20 Gb/s) Cray Gemini, 3 D torus (160 GB/s BW, <1 μs latency) Memory bandwidth/node The zebrafish phenome project will require significant contributions from engineering, physics, computational science, and GRID GPU-assisted supercomputing. We seek collaborations with interested zebrafish laboratories and are poised to create the necessary infrastructure for our community project. Morphometrics by micro. CT will have to be integrated with phenotyping results obtained by histology, confocal laser microscopy and nonmorphological phenotyping assays. Acknowledgements: This work has been supported by NIH (R 24 RR 017441) and the Department of Energy.