Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

15 Zitate (Scopus)

Abstract

Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for accurate delineation of particularly small lesions. Here, similar to these previous works, we model the distribution of healthy brain MRI to localize pathologies from erroneous reconstructions. However, to achieve improved reconstruction fidelity at higher resolutions, we learn to compress and reconstruct different frequency bands of healthy brain MRI using the laplacian pyramid. In a range of experiments comparing our method to different State-of-the-Art approaches on three different brain MR datasets with MS lesions and tumors, we show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales, which allows to effectively cope with different pathologies and lesion sizes using a single model.

OriginalspracheEnglisch
TitelMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Redakteure/-innenAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten552-561
Seitenumfang10
ISBN (Print)9783030597184
DOIs
PublikationsstatusVeröffentlicht - 2020
Veranstaltung23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Dauer: 4 Okt. 20208 Okt. 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12264 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Land/GebietPeru
OrtLima
Zeitraum4/10/208/10/20

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