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Authors: Dhanunjaya Mitta 1 ; Soumick Chatterjee 2 ; 3 ; 1 ; Oliver Speck 2 ; 4 ; 5 ; 6 and Andreas Nürnberger 3 ; 1

Affiliations: 1 Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany ; 2 Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany ; 3 Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany ; 4 Center for Behavioral Brain Sciences, Magdeburg, Germany ; 5 German Center for Neurodegenerative Disease, Magdeburg, Germany ; 6 Leibniz Institute for Neurobiology, Magdeburg, Germany

Keyword(s): Unsupervised Learning, Deep Learning, MRI Segmentation, Liver Segmentation.

Abstract: Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections. The loss for the segmentation output was calculated using soft N-Cuts and for the reconstruction output using SSIM. Conditional Random Fields were used as a post-processing step to fine-tune the results. The proposed method has shown promising results, with a dice coefficient of 0 .88 for the liver segmentation compared against manual segmentation. (More)

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Paper citation in several formats:
Mitta, D.; Chatterjee, S.; Speck, O. and Nürnberger, A. (2021). Upgraded W-Net with Attention Gates and Its Application in Unsupervised 3D Liver Segmentation. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-486-2; ISSN 2184-4313, pages 488-494. DOI: 10.5220/0010221504880494

@conference{icpram21,
author={Dhanunjaya Mitta. and Soumick Chatterjee. and Oliver Speck. and Andreas Nürnberger.},
title={Upgraded W-Net with Attention Gates and Its Application in Unsupervised 3D Liver Segmentation},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2021},
pages={488-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010221504880494},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Upgraded W-Net with Attention Gates and Its Application in Unsupervised 3D Liver Segmentation
SN - 978-989-758-486-2
IS - 2184-4313
AU - Mitta, D.
AU - Chatterjee, S.
AU - Speck, O.
AU - Nürnberger, A.
PY - 2021
SP - 488
EP - 494
DO - 10.5220/0010221504880494

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