Screening of Dementia on OCTA Images via Multi-projection Consistency and Complementarity

Wang，Xingyue1,2; Li，Heng2; Xiao，Zunjie1,2; Fu，Huazhu3; Zhao，Yitian4; Jin，Richu2; Zhang，Shuting5; Kwapong，William Robert5; Zhang，Ziyi5; Miao，Hanpei1,2; Liu，Jiang1,2,6

10.1007/978-3-031-16434-7_66

2022

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-16433-0

LECT NOTES ARTIF INT

13432 LNCS

688-698

SEP 18-22, 2022

null,Singapore,SINGAPORE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

It has been suggested that the retinal vasculature alternations are associated with dementia in recent clinical studies, and the eye examination may facilitate the early screening of dementia. Optical Coherence Tomography Angiography (OCTA) has shown its superiority in visualizing superficial vascular complex (SVC), deep vascular complex (DVC), and choriocapillaris, and it has been extensively used in clinical practice. However, the information in OCTA is far from fully mined by existing methods, which straightforwardly analyze the multiple projections of OCTA by average or concatenation. These methods do not take into account the relationship between multiple projections. Accordingly, a Multi-projection Consistency and complementarity Learning Network (MUCO-Net) is proposed in this paper to explore the diagnosis of dementia based on OCTA. Firstly, a consistency and complementarity attention (CsCp) module is developed to understand the complex relationships among various projections. Then, a cross-view fusion (CVF) module is introduced to combine the multi-scale features from the CsCp. In addition, the number of input flows of the proposed modules is flexible to boost the interactions across the features from different projections. In the experiment, MUCO-Net is implemented on two OCTA datasets to screen for dementia and diagnose fundus diseases. The effectiveness of MUCO-Net is demonstrated by its superior performance to state-of-the-art methods.

Deep learning Dementia Multi-projection OCTA

First ; Corresponding

English

CPCI-S

China Postdoctoral Science Foundation[2021M691437] ; National Natural Science Foundation of China[62101236] ; Guangdong Provincial Department of Education[2020ZDZX3043] ; Science and Technology Innovation Committee of Shenzhen City["20200925174052004","JCYJ20200109140820699"] ; Guangdong Provincial Key Laboratory[2020B121201001]

Computer Science ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000867288800066

2-s2.0-85139010248

Scopus

1.Research Institute of Trustworthy Autonomous Systems,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Agency for Science,Technology and Research (A*STAR),Singapore,Singapore
4.Cixi Institute of Biomedical Engineering,Chinese Academy of Sciences,Beijing,China
5.West China Hospital Sichuan University,Chengdu,China
6.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,China

Wang，Xingyue,Li，Heng,Xiao，Zunjie,et al. Screening of Dementia on OCTA Images via Multi-projection Consistency and Complementarity[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:688-698.