textRecent studies have shown the ability of confocal microscopy to noninvasively
image cells in vivo in real time. This ability to visualize nuclei in vivo
shows the potential of confocal microscopy to dramatically improve the
prevention, detection and therapy of epithelial cancers. More exciting is the
potential to quantitatively measure nuclear morphometry providing a basis to
automate the cancer detection process. This dissertation describes studies
exploring this potential in ex vivo cervical tissue using acetic acid as a nuclear
contrast agent.
First the use of acetic acid was demonstrated to improved contrast in
confocal images of cervical tissue sufficiently to allow segmentation.
Segmentation is robust throughout the epithelium in most normal tissue and upper
portions of tissue diagnosed with severe dysplasia. Based upon this segmentation,
quantitative feature measurements were extracted from confocal images of
cervical tissue in a pilot study to determine if the features would aide in the
detection of dysplasia. Simultaneously, a qualitative review of confocal images
was performed by untrained reviewers and compared with clinical colposcopic
impressions, the standard clinical tool aiding in dysplasia detection. The
sensitivity and specificity of both the qualitative (95% and 69%) and quantitative
(100% and 91%) review were improved compared to colposcopic review (91%
and 62%).
Finally the ability of confocal microscopy to produce 3D images was
explored as a further means to improve dysplasia detection. Based upon Beer’s
equation for light attenuation, the scattering coefficient was extracted from 3D
image sets of ex vivo cervical tissue and compared with histology from the same
precancerous lesion. The results suggested a possible correlation between high
scattering values and the presence of dysplasia. Quantitative 3D features were
also extracted from 3D image sets and correlated with the presence of CIN 2/3.
Increased separation between normal and CIN 2/3 biopsies was produced using
the 3D features as compared to the 2D. More importantly, when additional
information (scattering coefficient) is combined with the 2D features, the ability
to distinguish between normal and CIN 2/3 is 100% accurate in this small sample
set.Electrical and Computer Engineerin