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Horvath, I. ; Paetzold, J.C. ; Schoppe, O. ; Al-Maskari, R. ; Ezhov, I.* ; Shit, S.* ; Li, H.* ; Ertürk, A. ; Menze, B.*

METGAN: Generative tumour inpainting and modality synthesis in light sheet microscopy.

In: (2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 4-8 January 2022, Waikoloa). 2022. 3230-3240 (Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022)
DOI
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research. Yet, a massive lack of annotations prevents the broad use of deep learning to analyze such data. In this paper, we introduce a novel generative method which leverages real anatomical information to generate realistic image-label pairs of tumours. We construct a dualpathway generator, for the anatomical image and label, trained in a cycle-consistent setup, constrained by an independent, pretrained segmentor. Our method performs two concurrent tasks: domain adaptation and semantic synthesis, which, to our knowledge, has not been done before. The generated images yield significant quantitative improvement compared to existing methods that specialize in either of these tasks. To validate the quality of synthesis, we train segmentation networks on a dataset augmented with the synthetic data, substantially improving the segmentation over the baseline.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Autoencoders ; Gans ; Grouping And Shape ; Medical Imaging/imaging For Bioinformatics/biological And Cell Microscopy Deep Learning ; Neural Generative Models ; Segmentation
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
Konferenztitel 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Konferzenzdatum 4-8 January 2022
Konferenzort Waikoloa
Quellenangaben Band: , Heft: , Seiten: 3230-3240 Artikelnummer: , Supplement: ,
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505800-001
DOI 10.1109/WACV51458.2022.00329
Scopus ID 85126091417
Erfassungsdatum 2022-07-14
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