Name
Abstract | ||
|---|---|---|
Automated cell detection in histopathology images is a hard problem due to the large variance of cell shape and appearance. We show that cells can be detected reliably in images by predicting, for each pixel location, a monotonous function of the distance to the center of the closest cell. Cell centers can then be identified by extracting local extremums of the predicted values. This approach results in a very simple method, which is easy to implement. We show on two challenging microscopy image datasets that our approach outperforms state-of-the-art methods in terms of accuracy, reliability, and speed. We also introduce a new dataset that we will make publicly available. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24574-4_33 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Regression,Pattern recognition,Computer science,Ground truth,Pixel,Artificial intelligence | Conference | 9351 |
ISSN | Citations | PageRank |
0302-9743 | 19 | 0.74 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philipp Kainz | 1 | 98 | 6.15 |
| Martin Urschler | 2 | 347 | 23.94 |
| Samuel Schulter | 3 | 158 | 11.58 |
| Paul Wohlhart | 4 | 19 | 0.74 |
| Vincent Lepetit | 5 | 6178 | 306.48 |