Paper Info
Title
MaReIA: a cloud MapReduce based high performance whole slide image analysis framework
Abstract
AbstractRecent advancements in systematic analysis of high resolution whole slide images have increase efficiency of diagnosis, prognosis and prediction of cancer and important diseases. Due to the enormous sizes and dimensions of whole slide images, the analysis requires extensive computing resources which are not commonly available. Images have to be tiled for processing due to computer memory limitations, which lead to inaccurate results due to the ignorance of boundary crossing objects. Thus, we propose a generic and highly scalable cloud-based image analysis framework for whole slide images. The framework enables parallelized integration of image analysis steps, such as segmentation and aggregation of micro-structures in a single pipeline, and generation of final objects manageable by databases. The core concept relies on the abstraction of objects in whole slide images as different classes of spatial geometries, which in turn can be handled as text based records in MapReduce. The framework applies an overlapping partitioning scheme on images, and provides parallelization of tiling and image segmentation based on MapReduce architecture. It further provides robust object normalization, graceful handling of boundary objects with an efficient spatial indexing based matching method to generate accurate results. Our experiments on Amazon EMR show that MaReIA is highly scalable, generic and extremely cost effective by benchmark tests.
Year
DOI
Venue
2019
10.1007/s10619-018-7237-1
Periodicals
Keywords
Field
DocType
Whole slide images,Spatial application,Pathology image analysis,MapReduce,Cloud computing
Data mining,Normalization (statistics),Abstraction,Segmentation,Computer science,Search engine indexing,Image segmentation,Real-time computing,Computer memory,Cloud computing,Scalability
Journal
Volume
Issue
ISSN
37
2
0926-8782
Citations 
PageRank 
References 
0
0.34
11
Authors
7
Name
Order
Citations
PageRank
Hoang Vo122713.17
Jun Kong210617.74
Dejun Teng302.70
Yanhui Liang4166.50
Ablimit Aji527714.26
George Teodoro615022.18
Fusheng Wang7102679.28