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Liu, Y.* ; Wagner, S. ; Peng, T.

Multi-modality microscopy image style augmentation for nuclei segmentation.

J. Imaging 8:71 (2022)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Annotating microscopy images for nuclei segmentation by medical experts is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it can not only deal with different cell assay types and lighting conditions, but also with different imaging modalities, such as bright-field and fluorescence microscopy. Using disentangled representations for content and style, we can preserve the structure of the original image while altering its style during augmentation. We evaluate our data augmentation on the 2018 Data Science Bowl dataset consisting of various cell assays, lighting conditions, and imaging modalities. With our style augmentation, the segmentation accuracy of the two top-ranked Mask R-CNN-based nuclei segmentation algorithms in the competition increases significantly. Thus, our augmentation technique renders the downstream task more robust to the test data heterogeneity and helps counteract class imbalance without resampling of minority classes.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Data Augmentation ; Nuclei Segmentation ; Style Transfer
ISSN (print) / ISBN 2313-433X
e-ISSN 2313-433X
Zeitschrift Journal of Imaging
Quellenangaben Band: 8, Heft: 3, Seiten: , Artikelnummer: 71 Supplement: ,
Verlag MDPI
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen Munich School for Data Science (MUDS)