OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery

Zhao，Chengqian1,3,4; Li，Dengwang1; Feng，Cheng2; Li，Shuo3,4

2021-05-01

10.1016/j.media.2021.101982

MEDICAL IMAGE ANALYSIS   影响因子和分区

1361-8415

1361-8423

Fully automated comprehensive analysis of carotid artery (localization of range of interest (ROI), direct quantitative measurement and segmentation of lumen diameter (CALD) and intima-media thickness (CIMT), and motion estimation of the carotid wall) is a reliable auxiliary diagnosis of cardiovascular diseases, which relieves physicians from laborious workloads. No work has achieved fully automated comprehensive analysis of carotid artery due to five intractable challenges: (1) The heavy reliance on experienced carotid physicians for the selection of ROI limits fully automated studies. (2) The weak structural information of intima-media thickness increases the difficulty of feature encoding. (3) The radial motion of the carotid wall results in the lack of discriminant features of boundaries. (4) Diseased carotid arteries lose many expression features. (5) Optimal weights of multitask regression are hard to tune manually. In this paper, we propose a novel uncertainty-guided multitask regression network aided by optical flow named OF-UMRN to solve the intractable challenges. The four modules and three innovations of the OF-UMRN take their responsibility. OF-UMRN takes localization and mapping of ROI as a pre-processing. It achieves direct quantitative measurement and segmentation by a multitask regression network. And we creatively model homoscedastic uncertainty to automated tune the weights of the two tasks optimally. The OF-UMRN adopts a bidirectional mechanism to encode the optical flow used to predict the carotid wall's motion fields. More importantly, we creatively propose a dual optimization module based on the co-promotion between segmentation and motion estimation to improve the performance of radially moving and diseased carotid arteries. Therefore, the OF-UMRN makes the most of the pathological relationship between multiple objects and co-promotion between various tasks. Extensive experiments on US sequences of 101 patients have demonstrated the superior performance of OF-UMRN on the fully automated comprehensive analysis of the carotid artery. Therefore the OF-UMRN has excellent potential in clinical disease diagnoses and assessments of the carotid artery.

Bidirectional optical flow Direct quantitative measurement Dual optimization Homoscedastic uncertainty Motion estimation Segmentation

SCI ; EI

英语

其他

National Natural Science Foundation of China[61971271] ; Taishan Scholars Project of Shandong Province[Tsqn20161023] ; Primary Research and Development Plan of Shandong Province["2018GGX101018","2019QYTPY020"]

Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging

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

WOS:000639613600003

ELSEVIER

20210809944081

Automation ; Diagnosis ; Encoding (symbols) ; Motion estimation ; Uncertainty analysis

Medicine and Pharmacology:461.6 ; Data Processing and Image Processing:723.2 ; Automatic Control Principles and Applications:731 ; Light/Optics:741.1 ; Probability Theory:922.1

COMPUTER SCIENCE

2-s2.0-85101406234

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,China
2.Department of Ultrasound,The Second Affiliated Hospital,Southern University of Science and Technology,Shenzhen Third Peoples Hospital,Shenzhen,China
3.Department of Medical Imaging,Western University,London,Canada
4.Digital Imaging Group,London,Canada

Zhao，Chengqian,Li，Dengwang,Feng，Cheng,et al. OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery[J]. MEDICAL IMAGE ANALYSIS,2021,70.

Zhao，Chengqian,Li，Dengwang,Feng，Cheng,&Li，Shuo.(2021).OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery.MEDICAL IMAGE ANALYSIS,70.

Zhao，Chengqian,et al."OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery".MEDICAL IMAGE ANALYSIS 70(2021).