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2021 | OriginalPaper | Buchkapitel

Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation

verfasst von : Camila Gonzalez, Karol Gotkowski, Andreas Bucher, Ricarda Fischbach, Isabel Kaltenborn, Anirban Mukhopadhyay

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Verlag: Springer International Publishing

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Abstract

Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly.

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Titel: Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation
verfasst von: Camila Gonzalez
Karol Gotkowski
Andreas Bucher
Ricarda Fischbach
Isabel Kaltenborn
Anirban Mukhopadhyay
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87233-5

Electronic ISBN: 978-3-030-87234-2

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87234-2_29

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