Big Data Category

Harmonization of white and gray matter features in diffusion microarchitecture for cross sectional studies

Jun. 25, 2018—Prasanna Parvathaneni, Shunxing Bao , Allison Hainline , Yuankai Huo , Kurt G. Schilling , Hakmook Kang , Owen Williams , Neil D. Woodward , Susan M. Resnick , David H. Zald , Ilwoo Lyu , Bennett A. Landman “Harmonization of white and gray matter features in diffusion microarchitecture for cross sectional studies.” In International Conference on Clinical and Medical Image Analysis 2018 (ICCMIA’18) – Accepted Abstract Understanding of the specific processes...

Towards Portable Large-Scale Image Processing with High-Performance Computing

May. 8, 2018—Yuankai Huo, Justin Blaber, Stephen M. Damon, Brian D. Boyd, Shunxing Bao, Prasanna Parvathaneni, Camilo Bermudez Noguera, Shikha Chaganti, Vishwesh Nath, Greer M. Jasmine, Ilwoo Lyu, William R. French, Allen T. Newton, Baxter P. Rogers, Bennett A. Landman. “Towards Portable Large-Scale Image Processing with High-Performance Computing”. Journal of Digital Imaging. (2018): 1-11. Open Access Download...

Opportunities for Mining Radiology Archives for Pediatric Control Images

Dec. 17, 2017—Bermudez, C., Probst, V. N., Davis, L. T., Lasko, T., & Landman, B. A. (2017). Opportunities for Mining Radiology Archives for Pediatric Control Images. arXiv preprint arXiv:1712.02728. Full Text: https://arxiv.org/ftp/arxiv/papers/1712/1712.02728.pdf Abstract A large database of brain imaging data from healthy, normal controls is useful to describe physiologic and pathologic structural changes at a population scale....

Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features

May. 15, 2017—Citation: Bermudez, C. et.al. Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstrract It is well known that there are structural changes that occur in the brain with age. However, there are insufficient imaging biomarkers that reliably describe structural...

$Throughput analysis for each of the test scenarios. (A) presents the number of datasets processed per minute by each of the scenarios as a function of the number of datasets selected for processing. (B) shows the fraction of time spent on overhead relative to the number of datasets.$

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

Nov. 15, 2016—Shunxing Bao, Andrew Plassard, Bennett Landman and Aniruddha Gokhale. “Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.” IEEE International Conference on Cloud Engineering (IC2E), Vancouver, Canada, April 2017. Full text: NIHMSID Abstract Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval....

Hadoop and SGE data retrieval, processing and storage working flow basing on Multi-atlas CRUISE (MaCRUISE) segmentation [14, 15]. The data in an HBase table is approximately balanced to each Regionserver. The Regionserver collocates with a Hadoop Datanode to fully utilize the data collocation and locality[7]. We design our proposed computation models using only the map phase of Hadoop’s MapReduce [13]. In this phase, the data is retrieved locally; if the result were moved to reduce phase, more data movement would occur, because the reduce phase does not ensure process local data. Within the map phase, all necessary data is retrieved and saved on a local directory and gets furtherly processed by locally installed binary executables command-line program. After that, the results of processing are uploaded back to HBase. For SGE, the user submits a batch of jobs to a submit host, and this host dispatches the job to execution hosts. Each execution host retrieves the data within a shared NFS and stores the result back to the NFS.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine

Nov. 1, 2016—Shunxing Bao, Frederick D. Weitendorf, Andrew J. Plassard, Yuankai Huo, Aniruddha Gokhale, Bennett A. Landman. “Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine”. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Traditional large scale processing uses a cluster computer that combines a group of workstation nodes...

(A) A standard fully connected neural network where each layer's node is connected to each node from the
previous layer.7 (B) A convolutional neural network connecting a covolutional layer to a pooling layer.

Deep Learning for Brain Tumor Classification

Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...

Quality analysis reports generally perform first level analyses, including diffusion tensor imaging (A) (Asman and Landman, 2013; Lauzon et al., 2013b; Lauzon and Landman, 2013; Papadakis et al., 2003; Whitcher et al., 2008), func-
tional MRI (B) (Friedman and Glover, 2006), multi-atlas brain segmentation (C) (Asman and Landman, 2011, 2013; Ourselin et al., 2001, 2002), white matter labeling (D) (Plassard et al., 2015), registration to template spaces (E) (Fonov et al.,2009), FreeSurfer (F) (Fischl, 2012) and TRACULA (G) (Yendiki et al., 2011).

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment

Jan. 31, 2016—Robert L. Harrigan, Benjamin C. Yvernault, Brian D. Boyd, Stephen M. Damon, Kyla David Gibney, Benjamin N. Conrad, Nicholas S. Phillips, Baxter P. Rogers, Yurui Gao, Bennett A. Landman “Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment” Neuroimage, 2014. In press May 2015† Full Text:...

Fig-1-Flowchart-of-training-a-data-driven-dictionary-of-whole-brain-probabilistic

Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation

Oct. 4, 2015—Yuankai Huo, Katherine Swett, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman. “Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation”, MICCAI MAPPING Workshop, Munich, Germany, October 2015. Full text: https://www.researchgate.net/publication/303483865_Data-driven_Probabilistic_Atlases_Capture_Whole-brain_Individual_Variation Abstract

Overview of the need for accurate estimates in high performance medical imaging. Complex multi-stage imaging layouts need to provide accurate estimations of their resource requirements in order to leverage high performance computing resources. The upper-right inlay shows histograms of the runtime for a single software module (“File Collection Efficient Registration”) on datasets acquired on 21 individuals

Resource Estimation in High Performance Medical Image Computing

Oct. 31, 2014—Rueben Banalagay, Kelsie J. Covington, D.Mitch Wilkes, Bennett A. Landman. “Resource Estimation in High Performance Medical Image Computing.” Neuroinformatics. 2014 Oct;12(4):563-73. † PMC4381797 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24906466 Abstract Medical imaging analysis processes often involve the concatenation of many steps (e.g., multi-stage scripts) to integrate and realize advancements from image acquisition, image processing, and computational analysis. With the...

« Previous Page