OF-MSRN: Optical flow-auxiliary multi-task regression network for direct quantitative measurement, segmentation and motion estimation

Zhao，Chengqian1; Feng，Cheng2; Li，Dengwang1; Li，Shuo3,4

2020

34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence

2159-5399

2374-3468

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

34

1218-1225

FEB 07-12, 2020

null,New York,NY

2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module.

其他

英语

CPCI-S ; EI

Computer Science ; Education & Educational Research

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Education, Scientific Disciplines

WOS:000667722801036

20212110389759

Artificial intelligence ; Diagnosis ; Optical flows

Medicine and Pharmacology:461.6 ; Artificial Intelligence:723.4 ; Light/Optics:741.1

2-s2.0-85103401865

Scopus

1.Shandong Key Laboratory of Medical Physics and Image Processing,Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine,School of Physics and Electronics,Shandong Normal University,Jinan, Shandong,250358,China
2.Department of Ultrasound,Second Affiliated Hospital,Southern University of Science and Technology,Shenzhen Third People’s Hospital,Shenzhen, Guangdong,China
3.Department of Medical Imaging,Western University,London,Canada
4.Digital Image Group,London,Canada

Zhao，Chengqian,Feng，Cheng,Li，Dengwang,et al. OF-MSRN: Optical flow-auxiliary multi-task regression network for direct quantitative measurement, segmentation and motion estimation[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2020:1218-1225.