Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning.

ABSTRACT:

Background

Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear p

SUBMITTER: Cukierski WJ 

PROVIDER: S-EPMC3484015 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature