A NEW COLOR AUGMENTATION METHOD FOR DEEP LEARNING SEGMENTATION OF HISTOLOGICAL IMAGES

Yang Xiao 1 Etienne Decencière 1 Santiago Velasco-Forero 1 Hélène Burdin 2 Thomas Bornschlögl 3 Françoise Bernerd 3 Emilie Warrick 3 Thérese Baldeweck 3
1 CMM - Centre de Morphologie Mathématique
2 ADCIS - Société ADCIS
3 L'Oréal Recherche - L'Oréal Recherche France
Abstract : This paper addresses the problem of labeled data insufficiency in neural network training for semantic segmentation of color-stained histological images acquired via Whole Slide Imaging. It proposes an efficient image augmentation method to alleviate the demand for a large amount of labeled data and improve the network's generalization capacity. Typical image augmentation in bioimaging involves geometric transformation. Here, we propose a new image augmentation technique by combining the structure of one image with the color appearance of another image to construct augmented images on-the-fly for each training iteration. We show that it improves performance in the segmentation of histological images of human skin, and also offers better results when combined with geometric transformation .
Keywords : color-stained slide deep learning image segmentation color transfer histopathology Fontana Masson skin
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Contributeur : Etienne Decencière <>
Soumis le : vendredi 28 juin 2019 - 11:49:56
Dernière modification le : dimanche 7 juillet 2019 - 01:41:16

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Yang Xiao, Etienne Decencière, Santiago Velasco-Forero, Hélène Burdin, Thomas Bornschlögl, et al.. A NEW COLOR AUGMENTATION METHOD FOR DEEP LEARNING SEGMENTATION OF HISTOLOGICAL IMAGES. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), Apr 2019, Venise, France. ⟨hal-02167903⟩

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