Summary
Background: Automated analysis of imaged histopathology specimens could potentially provide support
for improved reliability in detection and classification in a range of investigative
and clinical cancer applications. Automated segmentation of cells in the digitized
tissue microarray (TMA) is often the prerequisite for quantitative analysis. However
overlapping cells usually bring significant challenges for traditional segmentation
algorithms.
Objectives: In this paper, we propose a novel, automatic algorithm to separate overlapping cells
in stained histology specimens acquired using bright-field RGB imaging.
Methods: It starts by systematically identifying salient regions of interest throughout the
image based upon their underlying visual content. The segmentation algorithm subsequently
performs a quick, voting based seed detection. Finally, the contour of each cell is
obtained using a repulsive level set deformable model using the seeds generated in
the previous step. We compared the experimental results with the most current literature,
and the pixel wise accuracy between human experts’ annotation and those generated
using the automatic segmentation algorithm.
Results: The method is tested with 100 image patches which contain more than 1000 overlapping
cells. The overall precision and recall of the developed algorithm is 90% and 78%,
respectively. We also implement the algorithm on GPU. The parallel implementation
is 22 times faster than its C/C++ sequential implementation.
Conclusion: The proposed segmentation algorithm can accurately detect and effectively separate
each of the overlapping cells. GPU is proven to be an efficient parallel platform
for overlapping cell segmentation.
Keywords
Parallel computing - segmentation - seed detection - pathology images
