Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans

Application and prospects of AI-based radiomics in ultrasound diagnosis

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

A Deep-Learning Enabled Automatic Fetal Thalamus Diameter Measurement Algorithm

The analysis of maternal factors that impact the normal development of the fetal thalamus is an emerging field of research and requires the retrospective measurement of fetal thalamus diameter (FTD). Unfortunately, FTD is not measured in routine 2D ultrasound (2D-US) screenings of fetuses. Manual measurement of FTD is a laborious, difficult, and error-prone process because the thalamus lacks well-defined boundaries in 2D-US images of the fetal brain as it has a similar echogenicity to the surrounding brain tissue. Traditional methods based on statistical shape models (SSMs) perform poorly in measuring FTD due to the noisy textures and fuzzy edges of the fetal thalamus in 2D-US images of the fetal brain. To overcome these difficulties, we propose a deep learning-based automatic FTD measurement algorithm, FTDNet. FTDNet measures FTD by learning to directly detect the measurement landmarks through supervised learning. The algorithm first detects the region of the brain that contains the thalamus structure, and then focuses on processing that region for FTD landmark detection. Our FTD dataset, developed through a consensus between two ultrasonographers, contains 1,111 pairs of landmark coordinates for measuring FTD and verified bounding boxes surrounding the fetal thalamus. To assess FTDNet's measurement consistency compared to the ground truth, we used the intraclass correlation coefficient (ICC). FTDNet achieved an ICC score of 0.734, significantly outperforming the prior SSM method and other baseline comparison methods. Our findings are an important step forward in understanding the maternal factors which influence fetal brain development.Clinical relevance- This work proposes an end-to-end thalamus detection and measurement algorithm for measuring fetal thalamus diameter. Our work represents a significant step in the research of how maternal factors can impact fetal thalamus development. The development of an automatic and accurate method for measuring FTD through deep learning has the potential to greatly advance this field of study.

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set

OBJECTIVE

Characterization of the optic nerve through measurement of optic nerve diameter (OND) and optic nerve sheath diameter (ONSD) using transorbital sonography (TOS) has proven to be a useful tool for the evaluation of intracranial pressure (ICP) and multiple neurological conditions. We describe a deep learning-based system for automatic characterization of the optic nerve from B-mode TOS images by automatic measurement of the OND and ONSD. In addition, we determine how the signal-to-noise ratio in two different areas of the image influences system performance.

METHODS

A UNet was trained as the segmentation model. The training was performed on a multidevice, multicenter data set of 464 TOS images from 110 subjects. Fivefold cross-validation was performed, and the training process was repeated eight times. The final prediction was made as an ensemble of the predictions of the eight single models. Automatic OND and ONSD measurements were compared with the manual measurements taken by an expert with a graphical user interface that mimics a clinical setting.

RESULTS

A Dice score of 0.719 ± 0.139 was obtained on the whole data set merging the test folds. Pearson's correlation was 0.69 for both OND and ONSD parameters. The signal-to-noise ratio was found to influence segmentation performance, but no clear correlation with diameter measurement performance was determined.

CONCLUSION

The developed system has a good correlation with manual measurements, proving that it is feasible to create a model capable of automatically analyzing TOS images from multiple devices. The promising results encourage further definition of a standard protocol for the automatization of the OND and ONSD measurement process using deep learning-based methods. The image data and the manual measurements used in this work will be available at 10.17632/kw8gvp8m8x.1.

Lateral M-Mode: Ultrasound Visualization of Displacement Along Longitudinal Direction at Intima-Media Complex

Quantification of the dynamics of the carotid artery wall is useful in evaluating arteriosclerosis and atherosclerosis. As the carotid artery wall moves not only in the radial direction but also in the longitudinal direction, longitudinal movement should be considered in the analysis of the dynamic properties of the carotid artery wall. In this study, we propose a "lateral M-mode" method for visualizing the longitudinal movement of the intima-media complex (IMC). For the lateral M-mode, we set the target line in the longitudinal direction along the IMC and visualize the signals on the target line frame-by-frame by correcting the position of the target line along the radial displacement estimated by the phased tracking method. Differentiating the envelope signals between consecutive ultrasound beams was effective in visualizing the lateral movement of the IMC. The precision of the longitudinal displacement of the IMC estimated using the conventional block-matching method was validated by comparing it with the lateral M-mode. Because the conventional M-mode sequence plays an important role in evaluation of the dynamics of various tissues, the proposed "lateral M-mode" contributes to a detailed understanding of vascular dynamics and the development of diagnostic methods for vascular diseases.

CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images

The intima-media thickness (IMT) is an effective biomarker for atherosclerosis, which is commonly measured by ultrasound technique. However, the intima-media complex (IMC) segmentation for the IMT is challenging due to confused IMC boundaries and various noises. In this paper, we propose a flexible method CSM-Net for the joint segmentation of IMC and Lumen in carotid ultrasound images. Firstly, the cascaded dilated convolutions combined with the squeeze-excitation module are introduced for exploiting more contextual features on the highest-level layer of the encoder. Furthermore, a triple spatial attention module is utilized for emphasizing serviceable features on each decoder layer. Besides, a multi-scale weighted hybrid loss function is employed to resolve the class-imbalance issues. The experiments are conducted on a private dataset of 100 images for IMC and Lumen segmentation, as well as on two public datasets of 1600 images for IMC segmentation. For the private dataset, our method obtain the IMC Dice, Lumen Dice, Precision, Recall, and F1 score of 0.814 ± 0.061, 0.941 ± 0.024, 0.911 ± 0.044, 0.916 ± 0.039, and 0.913 ± 0.027, respectively. For the public datasets, we obtain the IMC Dice, Precision, Recall, and F1 score of 0.885 ± 0.067, 0.885 ± 0.070, 0.894 ± 0.089, and 0.885 ± 0.067, respectively. The results demonstrate that the proposed method precedes some cutting-edge methods, and the ablation experiments show the validity of each module. The proposed method may be useful for the IMC segmentation of carotid ultrasound images in the clinic. Our code is publicly available at https://github.com/yuanyc798/US-IMC-code.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.