A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging-Reference-Cited by-同舟云学术

A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging

Published:2023-12-11 Issue: Volume: Page:
ISSN:
Container-title:RAiSE-2023
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Author:

Shreya ¹,Sushanth ¹,Shetty Dasharathraj K.¹^ORCID,Bhatta Shreepathy Ranga²,Panwar Nikita²

Affiliation:

1. Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India

2. Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/59/1/13/pdf

Reference23 articles.

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2. Patil, V., Saxena, J., Vineetha, R., Paul, R., Shetty, D.K., Sharma, S., Smriti, K., Singhal, D.K., and Naik, N. (2023). Age assessment through root lengths of mandibular second and third permanent molars using machine learning and Artificial Neural Networks. J. Imaging, 9.

3. Application of artificial intelligence-based classifiers to predict the outcome measures and stone-free status following percutaneous nephrolithotomy for staghorn calculi: Cross-validation of data and estimation of accuracy;Hameed;Eur. Urol.,2021

4. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Munich, Germany.

5. Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25–28). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.