Name
Title | ||
|---|---|---|
Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens |
Abstract | ||
|---|---|---|
Automated detection of prostate cancer in digitized H&E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H&E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver-operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2081366 | Proceedings of SPIE |
Keywords | Field | DocType |
histopathology,whole-slide imaging,computer-aided detection,prostate cancer | H&E stain,Computer vision,Histogram,Receiver operating characteristic,Computer science,Local binary patterns,Biopsy,Prostate cancer,Artificial intelligence,Random forest,Cancer | Conference |
Volume | ISSN | Citations |
9420 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 3 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geert Litjens | 1 | 996 | 50.79 |
| Babak Ehteshami Bejnordi | 2 | 720 | 30.27 |
| n timofeeva | 3 | 0 | 0.34 |
| ghedhban swadi | 4 | 0 | 0.34 |
| i kovacs | 5 | 0 | 0.34 |
| c hulsbergenvan de kaa | 6 | 2 | 0.75 |
| j van der laak | 7 | 0 | 0.34 |