Aug 14, 2015

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

Journal of Microscopy
P BajcsyM C Brady


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-stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state-of-the-art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate 'ground truth' of statistically representative samples and from visual inspection into the evaluation. The...Continue Reading

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Mentioned in this Paper

Biologic Segmentation
Projections and Predictions
Spatial Distribution
Evaluation Procedure
Cell Nucleus
Optical Image Reconstruction

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