Name
Abstract | ||
|---|---|---|
Nuclei counting in epithelial cells is an indication for tumor proliferation rate which is useful to rank tumors and select an appropriate treatment schedule for the patient. However, due to the high inter- and intra-observer variability in nuclei counting, pathologists seek a deterministic proliferation rate estimate. Histology tissue contains epithelial and stromal cells. However, nuclei counting is clinically restricted to epithelial cells because stromal cells do not become cancerous themselves since they remain genetically normal. Counting nuclei existing within the stromal tissue is one of the major causes of the proliferation rate non-deterministic estimation. Digitally removing stromal tissue will eliminate a major cause in pathologist counting variability and bring the clinical pathologist a major step closer toward a deterministic proliferation rate estimation. To that end, we propose a computer aided diagnosis (CAD) system for eliminating stromal cells from digital histology images based on the local binary patterns, entropy measurement, and statistical analysis. We validate our CAD system on a set of fifty Ki-67-stained histology images. Ki-67-stained histology images are among the clinically approved methods for proliferation rate estimation. To test our CAD system, we prove that the manual proliferation rate estimation performed by the expert pathologist does not change before and after stromal removal. Thus, stromal removal does not affect the expert pathologist estimation clinical decision. Hence, the successful elimination of the stromal area highly reduces the false positive nuclei which are the major confusing cause for the less experienced pathologists and thus accounts for the non-determinism in the proliferation rate estimation. Our experimental setting shows statistical insignificance (paired student t-test shows rho = 0.74) in the manual nuclei counting before and after our automated stromal removal. This means that the clinical decision of the expert pathologist is not affected by our CAD system which is what we want to prove. However, the usage of our CAD system substantially account for the reduced inter- and intra-proliferation rate estimation variability and especially for less-experienced pathologists. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911007 | Proceedings of SPIE |
Keywords | Field | DocType |
Digital Histology,Breast Cancer Prognosis,Proliferation rate,CAD,Image Segmentation | Stromal cell,Computer-aided diagnosis,Local binary patterns,Artificial intelligence,Cad system,Treatment Schedule,Histology,CAD,Computer vision,Simulation,Radiology,Statistical analysis,Physics | Conference |
Volume | ISSN | Citations |
8315 | 0277-786X | 1 |
PageRank | References | Authors |
0.39 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raja' S. Alomari | 1 | 167 | 16.03 |
| Subarna Ghosh | 2 | 73 | 5.40 |
| Vipin Chaudhary | 3 | 838 | 83.24 |
| Omar S. Al-Kadi | 4 | 32 | 4.32 |