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Kofler, F. ; Ezhov, I.* ; Fidon, L.* ; Horvath, I. ; de la Rosa, E.* ; LaMaster, J.* ; Li, H.* ; Finck, T. ; Shit, S.* ; Paetzold, J.C. ; Bakas, S.* ; Piraud, M. ; Kirschke, J.* ; Vercauteren, T.* ; Zimmer, C.* ; Wiestler, B.* ; Menze, B.*

Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings.

In: (Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries). Berlin [u.a.]: Springer, 2023. 3-13 (Lect. Notes Comput. Sc. ; 13769 LNCS)
Verlagsversion DOI
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We evaluate on a complex multi-class segmentation problem, specifically glioma segmentation, following the BraTS annotation protocol. The training data features quality ratings from 15 expert neuroradiologists on a scale ranging from 1 to 6 stars for various computer-generated and manual 3D annotations. Even though the networks operate on 2D images and with scarce training data, we can approximate segmentation quality within a margin of error comparable to human intra-rater reliability. Segmentation quality prediction has broad applications. While an understanding of segmentation quality is imperative for successful clinical translation of automatic segmentation quality algorithms, it can play an essential role in training new segmentation models. Due to the split-second inference times, it can be directly applied within a loss function or as a fully-automatic dataset curation mechanism in a federated learning setting.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Automatic Quality Control ; Brats ; Glioma ; Quality Estimation ; Segmentation Quality Metrics
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 13769 LNCS, Heft: , Seiten: 3-13 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
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-530001-001
G-505800-001
Förderungen
AIME GPU cloud services
NIH/NINDS
National Institutes of Health (NIH)
Helmut Horten Foundation
ERC, DFG, BMBF
Graduate School of Bioengineering, Technical University of Munich
Technical University of Munich - Institute for Advanced Study - German Excellence Initiative
Anna Valentina Lioba Eleonora Claire Javid Mamasani
Translational Brain Imaging Training Network (TRABIT) under the European Union's 'Horizon 2020' research & innovation program
Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE)
DOI 10.1007/978-3-031-33842-7_1
WOS ID 001116070400001
Scopus ID 85172195909
Erfassungsdatum 2023-10-18
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