Advances in high-speed, three-dimensional imaging and automated segmentation algorithms for thick and overlapped clusters in cytologic preparations. Application to cervical smears.
OBJECTIVE
To use three-dimensional (3-D) imaging and localized adaptive image analysis to enable automated cervical smear screening systems to efficiently and effectively process thick and overlapped cell clusters currently left unprocessed.


METHODS
Instrumentation was developed to perform high-speed (50-200 optical sections per second at 256 x 256 resolution), 3-D imaging of thick regions of cervical smears. Normal and abnormal ThinPrep smears were imaged at two levels of resolution to approximate higher-resolution, wide-area imaging. Improved dual-resolution, 3-D image analysis algorithms were developed for segmenting nuclei in these clusters.


RESULTS
Despite low contrast, high variability and dense overlaps, the algorithms detected 89% and correctly segmented 76% of nuclei in clusters from normal smears and detected 75% and correctly segmented 45% of nuclei in clusters from abnormal smears in low-resolution images. In high-resolution images they detected 88% and segmented 76% of nuclei from normal specimens and detected 55% and segmented 45% of nuclei from abnormal specimens. At least one nucleus from each cell cluster was correctly segmented.


CONCLUSIONS
Selective application of 3-D imaging and 3-D image analysis to thick and overlapped regions can enable a significant fraction (45-89%) of clustered and embedded cells to be accessed by an automated analysis system. These regions are, for the most part, unprocessable by current two-dimensional methods.
Mackin
R W
RW
Electrical, Computer and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA.
Newton
L M
LM
Turner
J N
JN
Roysam
B
B
D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014626:Vaginal Smears