Frontiers of Information Technology & Electronic Engineering
>> 2022,
Volume 23,
Issue 9
doi:
10.1631/FITEE.2200102
Visual recognition of cardiac pathology based on 3D parametric model reconstruction
Affiliation(s): School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York 10032, USA; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; less
Received: 2022-03-16
Accepted: 2022-09-21
Available online: 2022-09-21
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Abstract
Visual recognition of cardiac images is important for 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 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, s are constructed through the statistical shape model (SSM), and then the heart data are augmented via the variation in shape parameters of one 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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