Big Data Category
Anatomical context improves deep learning on the brain age estimation task
Jul. 12, 2019—Bermudez, C., Plassard, A. J., Chaganti, S., Huo, Y., Aboud, K. E., Cutting, L. E., … & Landman, B. A. (2019). Anatomical context improves deep learning on the brain age estimation task. Magnetic Resonance Imaging. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/31247249 Abstract Deep learning has shown remarkable improvements in the analysis of medical images without the need for...
Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network
Dec. 10, 2018—Cailey I. Kerley, Yuankai Huo, Shikha Chaganti, Shunxing Bao, Mayur B. Patel, Bennett A. Landman. “Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network.” In SPIE Medical Imaging, International Society for Optics and Photonics, 2019. Full text: NIHMSID Abstract Brain imaging analysis on clinically acquired computed tomography (CT) is essential...
Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing
Oct. 26, 2018—Shunxing Bao, Prasanna Parvathaneni, Yuankai Huo, Yogesh Barve, Andrew J. Plassard, Yuang Yao, Hongyang Sun, Ilwoo Lyu, David H. Zald, Bennett A. Landman and Aniruddha Gokhale. “Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing.” Big Data (Big Data), 2018 IEEE International Conference. (accepted) (acceptance rate 18.9%) Full text: TBD Abstract Big data medical image processing...
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...
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.](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2304/2016/11/14140802/Screen-Shot-2017-02-08-at-1.11.13-PM.png)
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...
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...