Biomedical image analysis and processing in clouds

Bednarz, Tomasz; Szul, Piotr; Arzhaeva, Yulia; Wang, Dadong; Burdett, Neil; Khassapov, Alex; Chen, Jinjun; Vallotton, Pascal; Lagerstrom, Ryan; Gureyev, T. E.; Taylor, John A.

doi:10.1063/1.4824998

Cited by 3 publications

(1 citation statement)

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“…Parsonson et al [17] create an image processing framework that exploits cloud resources for tasks such as volume rendering; however, unlike our work they focus on creating single-instance environments for multiple researchers and clinicians to access collaboratively. Bednarz et al [18] present an image analysis toolkit that enables access to the cutting edge analysis tools on cloud resources. Rather than build services or leverage scalable infrastructure, the authors instead focus on providing accessible interfaces to predeployed software packages on a cloud VM.…”

Section: Related Workmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

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We describe a cloud-based medical image reconstruction service designed to meet a real-time and daily demand to reconstruct thousands of images from proton cancer treatment facilities worldwide. Rapid reconstruction of a three-dimensional Proton Computed Tomography (pCT) image can require the transfer of 100GB of data and use of approximately 120 GPU-enabled compute nodes. The nature of proton therapy means that demand for such a service is sporadic and comes from potentially hundreds of clients worldwide. We thus explore the use of a commercial cloud as a scalable and cost-efficient platform for pCT reconstruction. To address the high performance requirements of this application we leverage Amazon Web Services' GPU-enabled cluster resources that are provisioned with high performance networks between nodes. To support episodic demand, we develop an on-demand multi-user provisioning service that can dynamically provision and resize clusters based on image reconstruction requirements, priorities, and wait times. We compare the performance of our pCT reconstruction service running on commercial cloud resources with that of the same application on dedicated local high performance computing resources. We show that we can achieve scalable and on-demand reconstruction of large scale pCT images for simultaneous multi-client requests, processing images in less than ten minutes for less than $10 per image.

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Section: Related Workmentioning

confidence: 99%

Scalable pCT Image Reconstruction Delivered as a Cloud Service

Chard

Madduri

Karonis

et al. 2018

IEEE Trans. Cloud Comput.

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Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks

Bednarz

Wang

Arzhaeva

et al. 2014

Advances in Experimental Medicine and Biology

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This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

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