CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-07-28
Cited: 0
Clicked: 2506
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-9666-2943
Jinxiao XIAO, Yansong LI, Yun TIAN, Dongrong XU, Penghui LI, Shifeng ZHAO, Yunhe PAN. Visual recognition of cardiac pathology based on 3D parametric model reconstruction[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1324-1337.
@article{title="Visual recognition of cardiac pathology based on 3D parametric model reconstruction",
author="Jinxiao XIAO, Yansong LI, Yun TIAN, Dongrong XU, Penghui LI, Shifeng ZHAO, Yunhe PAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="9",
pages="1324-1337",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200102"
}
%0 Journal Article
%T Visual recognition of cardiac pathology based on 3D parametric model reconstruction
%A Jinxiao XIAO
%A Yansong LI
%A Yun TIAN
%A Dongrong XU
%A Penghui LI
%A Shifeng ZHAO
%A Yunhe PAN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 9
%P 1324-1337
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200102
TY - JOUR
T1 - Visual recognition of cardiac pathology based on 3D parametric model reconstruction
A1 - Jinxiao XIAO
A1 - Yansong LI
A1 - Yun TIAN
A1 - Dongrong XU
A1 - Penghui LI
A1 - Shifeng ZHAO
A1 - Yunhe PAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 9
SP - 1324
EP - 1337
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200102
Abstract: Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment. Due to the limited availability of annotated datasets, traditional methods usually extract features directly from two-dimensional slices of three-dimensional (3D) heart images, followed by pathological classification. This process may not ensure the overall anatomical consistency in 3D heart. A new method for classification of cardiac pathology is therefore proposed based on 3D parametric model reconstruction. First, 3D heart models are reconstructed based on multiple 3D volumes of cardiac imaging data at the end-systole (ES) and end-diastole (ED) phases. Next, based on these reconstructed 3D hearts, 3D parametric models are constructed through the statistical shape model (SSM), and then the heart data are augmented via the variation in shape parameters of one 3D parametric model with visual knowledge constraints. Finally, shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology. Comprehensive experiments on the automated cardiac diagnosis challenge (ACDC) dataset of the Statistical Atlases and Computational Modelling of the Heart (STACOM) workshop confirm the superior performance and efficiency of this proposed approach.
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