| SYMPOSIUM - ORIGINAL ARTICLE |
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| Year : 2013 | Volume
: 4
| Issue : 1 | Page : 9 |
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Classification of mitotic figures with convolutional neural networks and seeded blob features
Christopher D Malon, Eric Cosatto
Department of Machine Learning, NEC Laboratories, America 4 Independence Way, Suite 200, Princeton, NJ 08540, USA
Correspondence Address:
Christopher D Malon
Department of Machine Learning, NEC Laboratories, America 4 Independence Way, Suite 200, Princeton, NJ 08540
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2153-3539.112694
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Background: The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR) challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral). Methods: Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN). The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. Results : On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. Conclusions : We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign. |
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