Segmentation of confocal microscope images of cell nuclei in thick tissue sections

Segmentation of intact cell nuclei from three-dimensional (3D) images of th ick tissue sections is an important basic capability necessary for many bio logical research studies, However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D s egmentation approach that combines the recognition capabilities of the huma n visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This incl udes a novel step, based on the Hough transform and an automatic focusing a lgorithm to estimate the size of nuclei, Then, using an interactive display each nuclear region is shown to the analyst, who classifies itt as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or deb ris, next, automatic image analysis based on morphological reconstruction a nd the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst ind icates which partial nuclei should be joined to form complete nuclei, The a pproach was assessed by calculating the fraction of correctly segmented nuc lei for a variety of tissue types: Caenorhabditis elegans embryos (839 corr ect out of a total of 848), normal human skin (343/362), benign human breas t tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335), Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.