KEYWORDS: Prostate, Tissues, Magnetic resonance imaging, Image segmentation, Prostate cancer, Cancer, Image processing, In vivo imaging, Data modeling, Pathology
We have developed an automatic technique to measure cell density in high resolution histopathology images of the
prostate, allowing for quantification of differences between tumour and benign regions of tissue. Haemotoxylin and
Eosin (H&E) stained histopathology slides from five patients were scanned at 20x magnification and annotated by an
expert pathologist. Colour deconvolution and a radial symmetry transform were used to detect cell nuclei in the images,
which were processed as a set of small tiles and combined to produce global maps of cell density. Kolmogorov-Smirnov
tests showed a significant difference in cell density distribution between tumour and benign regions of tissue for all
images analyzed (p < 0.05), suggesting that cell density may be a useful feature for segmenting tumour in un-annotated
histopathology images. ROC curves quantified the potential utility of cell density measurements in terms of specificity
and sensitivity and threshold values were investigated for their classification accuracy. Motivation for this work derives
from a larger study in which we aim to correlate ground truth histopathology with in-vivo multiparametric MRI
(mpMRI) to validate tumour location and tumour characteristics. Specifically, cell density maps will be registered with
T2-weighted MRI and ADC maps from diffusion-weighted MRI. The validated mpMRI data will then be used to
parameterise a radiobiological model for designing focal radiotherapy treatment plans for prostate cancer patients.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.