Image segmentation evaluation for very-large datasets

Anthony P. Reeves; Shuang Liu; Yiting Xie

doi:10.1117/12.2217331

24 March 2016 Image segmentation evaluation for very-large datasets

Anthony P. Reeves, Shuang Liu, Yiting Xie

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97853J (2016) https://doi.org/10.1117/12.2217331
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Anthony P. Reeves, Shuang Liu, and Yiting Xie "Image segmentation evaluation for very-large datasets", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97853J (24 March 2016); https://doi.org/10.1117/12.2217331

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available