Large-scale validation of a computer-aided polyp detection algorithm for CT colonography using cluster computing

Ingmar Bitter; John E. Brown; Daniel Brickman; Ronald M. Summers

doi:10.1117/12.536917

30 April 2004 Large-scale validation of a computer-aided polyp detection algorithm for CT colonography using cluster computing

Ingmar Bitter, John E. Brown, Daniel Brickman, Ronald M. Summers

Author Affiliations +

Proceedings Volume 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images; (2004) https://doi.org/10.1117/12.536917
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

The presented method significantly reduces the time necessary to validate a computed tomographic colonography (CTC) computer aided detection (CAD) algorithm of colonic polyps applied to a large patient database. As the algorithm is being developed on Windows PCs and our target, a Beowulf cluster, is running on Linux PCs, we made the application dual platform compatible using a single source code tree. To maintain, share, and deploy source code, we used CVS (concurrent versions system) software. We built the libraries from their sources for each operating system. Next, we made the CTC CAD algorithm dual-platform compatible and validate that both Windows and Linux produced the same results. Eliminating system dependencies was mostly achieved using the Qt programming library, which encapsulates most of the system dependent functionality in order to present the same interface on either platform. Finally, we wrote scripts to execute the CTC CAD algorithm in parallel. Running hundreds of simultaneous copies of the CTC CAD algorithm on a Beowulf cluster computing network enables execution in less than four hours on our entire collection of over 2400 CT scans, as compared to a month a single PC. As a consequence, our complete patient database can be processed daily, boosting research productivity. Large scale validation of a computer aided polyp detection algorithm for CT colonography using cluster computing significantly improves the round trip time of algorithm improvement and revalidation.

Citation Download Citation

Ingmar Bitter, John E. Brown, Daniel Brickman, and Ronald M. Summers "Large-scale validation of a computer-aided polyp detection algorithm for CT colonography using cluster computing", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); https://doi.org/10.1117/12.536917

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