Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies. (November 2015)

• Record Type: Journal Article
• Authors: Minervini, Massimo; Rusu, Cristian; Damiano, Mario; Tucci, Valter; Bifone, Angelo; Gozzi, Alessandro; Tsaftaris, Sotirios A
• Is Part Of: International journal of high performance computing applications. Volume 29:Number 4(2015:Winter)
• Page Start: 473
• Page End: 488
• Publication Date: 2015-11
• Subjects: High-performance computing -- cloud -- neuroimaging -- MRI -- phenotyping -- computer vision -- image analysis -- resource allocation -- machine learning; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342013519483
• Languages: English
• ISSNs: 1094-3420

Abstract

The combined use of mice that have genetic mutations (transgenic mouse models) of human pathology and advanced neuroimaging methods (such as magnetic resonance imaging) has the potential to radically change how we approach disease understanding, diagnosis and treatment. Morphological changes occurring in the brain of transgenic animals as a result of the interaction between environment and genotype can be assessed using advanced image analysis methods, an effort described as 'mouse brain phenotyping'. However, the computational methods involved in the analysis of high-resolution brain images are demanding. While running such analysis on local clusters is possible, not all users have access to such infrastructure and even for those that do, having additional computational capacity can be beneficial (e.g. to meet sudden high throughput demands). In this paper we use a commercial cloud platform for brain neuroimaging and analysis. We achieve a registration-based multi-atlas, multi-template anatomical segmentation, normally a lengthy-in-time effort, within a few hours. Naturally, performing such analyses on the cloud entails a monetary cost, and it is worthwhile identifying strategies that can allocate resources intelligently. In our context a critical aspect is the identification of how long each job will take. We propose a method that estimates the complexity of an image-processing task, a registration, using statistical moments and shape descriptors of the image content. We use this information to learn and predict the completion time of a registration. The proposed approach is easy to deploy, and could serve as an alternative for laboratories that may require instant access to large high-performance-computing infrastructures. To facilitate adoption from the community we publicly release the source code.