Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images

Zhou, Ran; Guo, Fumin; Azarpazhooh, M. Reza; Hashemi, Seyed Yaser; Xiao, Cheng; Spence, J. David; Ding, Mingyue; Fenster, Aaron

doi:10.1109/jbhi.2021.3060163

Cited by 49 publications

(34 citation statements)

References 36 publications

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“…Ikeda et al used three ethnic groups’ (Japanese, Italy and Hong Kong) data for IMT measurement at the bulb area [ 62 ]. Another group of Zhou et al [ 47 ] presented a deep learning-based method for segmentation of atherosclerotic plaque from carotid ultrasound images. Their system used two ethnic databases: namely those of Stroke Prevention and Atherosclerosis Research Centre (SPARC), from London, Canada; and Chinese data from Zhongnan Hospital (Wuhan, China).…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning techniques involve less human intervention and rely on directly extracting features from the images [ 43 ]. Several studies have been attempted recently using DL in medical imaging [ 44 , 45 , 46 , 47 ]. However, the current DL models use training and testing databases from the same ethnic group or cohort in recent studies.…”

Section: Introductionmentioning

confidence: 99%

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Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

et al. 2021

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Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

et al. 2021

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“…As a 3D measurement, VWV has a comparable dynamic range as TPV, 16 and therefore, serves as a strong alternative for carotid disease quantification in placebo‐controlled clinical trials investigating the effects of medical and dietary interventions 13,17 ; (3) Plaque segmentation is more difficult to automate. Existing plaque segmentation algorithms require users to identify a region of interest (ROI) for segmenting each plaque (i.e., carotid arteries with multiple plaques would require ROI identification multiple times) 18,19 . The initialization process would be even more time‐consuming for 3D analysis, in which a user would be required to crop an ROI on multiple image slices encompassing each plaque.…”

Section: Introductionmentioning

confidence: 99%

“…Existing plaque segmentation algorithms require users to identify a region of interest (ROI) for segmenting each plaque (i.e., carotid arteries with multiple plaques would require ROI identification multiple times). 18,19 The initialization process would be even more time-consuming for 3D analysis, in which a user would be required to crop an ROI on multiple image slices encompassing each plaque.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

2021

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Purpose Vessel wall volume (VWV) and localized vessel‐wall‐plus‐plaque thickness (VWT) measured from three‐dimensional (3D) ultrasound (US) carotid images are sensitive to anti‐atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen‐intima (LIB) and media‐adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Methods Segmentation for CCA and ICA was performed independently using the proposed two‐channel U‐Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test‐time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel‐wise majority voting. Results Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. Conclusions The proposed two‐channel U‐Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

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“…To provide additional context, the inclusion of flow information alongside the B-mode image has been proposed [8,9,10,11]. In recent years, deep learning has received increased focus [2,12], with the majority of work on vessel segmentation utilizing UNet/VNet-like models operating on individual frames [13,14].…”

Section: Introductionmentioning

confidence: 99%

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Jiang¹,

Chen²,

Bharat³

et al. 2021

Preprint

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Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while jointly utilizing information from Bmode and Color Doppler signals. Evaluating on femoral and tibial artery scans performed on healthy subjects by an expert ultrasonographer, and comparing to consensus expert ground-truth annotations of inner lumen boundaries, we demonstrate real-time segmentation using the contextaware models and show that they significantly outperform comparable baseline approaches.

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Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images

Cited by 49 publications

References 36 publications

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

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