Cell density in prostate histopathology images as a measure of tumor distribution
Hayley M. Reynolds, Scott Williams, Alan M. Zhang, Cheng Soon Ong, David Rawlinson, Rajib Chakravorty, Catherine A Mitchell, Annette Haworth · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.