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2021 | OriginalPaper | Buchkapitel

Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation

verfasst von : Seung Yeon Shin, Sungwon Lee, Ronald M. Summers

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

Verlag: Springer International Publishing

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Abstract

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.

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Vorheriges Kapitel Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation

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Titel: Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation
verfasst von: Seung Yeon Shin
Sungwon Lee
Ronald M. Summers
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87198-7

Electronic ISBN: 978-3-030-87199-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87199-4_27

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