TY - GEN
T1 - FusionNet
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2023
AU - Chang, Chujie
AU - Miyauchi, Shoko
AU - Morooka, Ken’ichi
AU - Kurazume, Ryo
AU - Mozos, Oscar Martinez
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40–60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four-dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git.
AB - Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40–60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four-dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git.
UR - http://www.scopus.com/inward/record.url?scp=85185719639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85185719639&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47425-5_4
DO - 10.1007/978-3-031-47425-5_4
M3 - Conference contribution
AN - SCOPUS:85185719639
SN - 9783031474248
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 35
EP - 44
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops - MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Woo, Jonghye
A2 - Hering, Alessa
A2 - Silva, Wilson
A2 - Li, Xiang
A2 - Fu, Huazhu
A2 - Liu, Xiaofeng
A2 - Xing, Fangxu
A2 - Purushotham, Sanjay
A2 - Mathai, T.S.
A2 - Mukherjee, Pritam
A2 - De Grauw, Max
A2 - Beets Tan, Regina
A2 - Corbetta, Valentina
A2 - Kotter, Elmar
A2 - Reyes, Mauricio
A2 - Baumgartner, C.F.
A2 - Li, Quanzheng
A2 - Leahy, Richard
A2 - Dong, Bin
A2 - Chen, Hao
A2 - Huo, Yuankai
A2 - Lv, Jinglei
A2 - Xu, Xinxing
A2 - Li, Xiaomeng
A2 - Mahapatra, Dwarikanath
A2 - Cheng, Li
A2 - Petitjean, Caroline
A2 - Presles, Benoît
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 October 2023 through 12 October 2023
ER -