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Proceedings Paper

Image segmentation evaluation for very-large datasets
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Paper Abstract

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

Paper Details

Date Published: 24 March 2016
PDF: 11 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853J (24 March 2016); doi: 10.1117/12.2217331
Show Author Affiliations
Anthony P. Reeves, Cornell Univ. (United States)
Shuang Liu, Cornell Univ. (United States)
Yiting Xie, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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