DeepRecon : Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method
(2022) 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13434 LNCS. p.567-577- Abstract
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental in building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures.... (More)
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental in building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures using the optimal latent representation. We further explore downstream applications of 3D shape reconstruction and 4D motion pattern adaptation by the different latent-space manipulation strategies. The simultaneously generated high-resolution images present a high interpretable value to assess the cardiac shape and motion. Experimental results demonstrate the effectiveness of our approach on multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D motion pattern adaption performance.
(Less)
- author
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- 3D reconstruction, Cardiac MRI, GAN, Latent space
- host publication
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Wang, Linwei ; Dou, Qi ; Fletcher, P. Thomas ; Speidel, Stefanie and Li, Shuo
- volume
- 13434 LNCS
- pages
- 11 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
- conference location
- Singapore, Singapore
- conference dates
- 2022-09-18 - 2022-09-22
- external identifiers
-
- scopus:85138994034
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783031164392
- DOI
- 10.1007/978-3-031-16440-8_54
- language
- English
- LU publication?
- yes
- id
- e3fed725-51ec-482e-b2ff-0d3d21bc716e
- date added to LUP
- 2023-01-16 11:00:59
- date last changed
- 2024-04-17 20:24:02
@inproceedings{e3fed725-51ec-482e-b2ff-0d3d21bc716e,
abstract = {{<p>Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental in building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and reconstructing the 3D volume are challenging. In this study, we propose an end-to-end latent-space-based framework, DeepRecon, that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Our method identifies the optimal latent representation of the cine image that contains accurate semantic information for cardiac structures. In particular, our model jointly generates synthetic images with accurate semantic information and segmentation of the cardiac structures using the optimal latent representation. We further explore downstream applications of 3D shape reconstruction and 4D motion pattern adaptation by the different latent-space manipulation strategies. The simultaneously generated high-resolution images present a high interpretable value to assess the cardiac shape and motion. Experimental results demonstrate the effectiveness of our approach on multiple fronts including 2D segmentation, 3D reconstruction, downstream 4D motion pattern adaption performance.</p>}},
author = {{Chang, Qi and Yan, Zhennan and Zhou, Mu and Liu, Di and Sawalha, Khalid and Ye, Meng and Zhangli, Qilong and Kanski, Mikael and Al’Aref, Subhi and Axel, Leon and Metaxas, Dimitris}},
booktitle = {{Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings}},
editor = {{Wang, Linwei and Dou, Qi and Fletcher, P. Thomas and Speidel, Stefanie and Li, Shuo}},
isbn = {{9783031164392}},
issn = {{1611-3349}},
keywords = {{3D reconstruction; Cardiac MRI; GAN; Latent space}},
language = {{eng}},
pages = {{567--577}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{DeepRecon : Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method}},
url = {{http://dx.doi.org/10.1007/978-3-031-16440-8_54}},
doi = {{10.1007/978-3-031-16440-8_54}},
volume = {{13434 LNCS}},
year = {{2022}},
}