A method for the evaluation of thousands of automated 3D stem cell segmentations.

ABSTRACT: There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three-dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z-stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z

SUBMITTER: Bajcsy P 

PROVIDER: S-EPMC4888372 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature