Using Anatomical Priors for Deep 3D One-shot Segmentation

Duc Pham, Gurbandurdy Dovletov, Josef Pauli

Abstract

With the success of deep convolutional neural networks for semantic segmentation in the medical imaging domain, there is a high demand for labeled training data, that is often not available or expensive to acquire. Training with little data usually leads to overfitting, which prohibits the model to generalize to unseen problems. However, in the medical imaging setting, image perspectives and anatomical topology do not vary as much as in natural images, as the patient is often instructed to hold a specific posture to follow a standardized protocol. In this work we therefore investigate the one-shot segmentation capabilities of a standard 3D U-Net architecture in such setting and propose incorporating anatomical priors to increase the segmentation performance. We evaluate our proposed method on the example of liver segmentation in abdominal CT volumes.

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Paper Citation


in Harvard Style

Pham D., Dovletov G. and Pauli J. (2021). Using Anatomical Priors for Deep 3D One-shot Segmentation.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-490-9, pages 174-181. DOI: 10.5220/0010303101740181


in Bibtex Style

@conference{bioimaging21,
author={Duc Pham and Gurbandurdy Dovletov and Josef Pauli},
title={Using Anatomical Priors for Deep 3D One-shot Segmentation},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2021},
pages={174-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010303101740181},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - Using Anatomical Priors for Deep 3D One-shot Segmentation
SN - 978-989-758-490-9
AU - Pham D.
AU - Dovletov G.
AU - Pauli J.
PY - 2021
SP - 174
EP - 181
DO - 10.5220/0010303101740181