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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Acknowledgments
We thank Dr. James Gulley for patient referral and for providing access to CT scans. This research was supported by the National Institutes of Health, Clinical Center.
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Shin, S.Y., Lee, S., Summers, R.M. (2021). Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12903. Springer, Cham. https://doi.org/10.1007/978-3-030-87199-4_27
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DOI: https://doi.org/10.1007/978-3-030-87199-4_27
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