Segmentation of carotid arterial walls using neural networks

PET/MR imaging of inflammation in atherosclerosis

Myocardial infarction, stroke, mental disorders, neurodegenerative processes, autoimmune diseases, cancer and the human immunodeficiency virus impact the haematopoietic system, which through immunity and inflammation may aggravate pre-existing atherosclerosis. The interplay between the haematopoietic system and its modulation of atherosclerosis has been studied by imaging the cardiovascular system and the activation of haematopoietic organs via scanners integrating positron emission tomography and resonance imaging (PET/MRI). In this Perspective, we review the applicability of integrated whole-body PET/MRI for the study of immune-mediated phenomena associated with haematopoietic activity and cardiovascular disease, and discuss the translational opportunities and challenges of the technology.

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Purpose

To develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI).

Methods

MRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD).

Results

The proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland–Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads.

Conclusion

The proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification.

Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks

Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the number of kernels. Auto encoder–decoder performs pixel wise classification using two interconnected pathways for identifying the boundary of lumen-intima (LI) and media adventitia (MA). This helps in reconstruction of the segmented portion for measurement of IMT. Both methods are tested using a dataset of 550 subjects. The results clearly indicate that end-to-end model has an edge over the pipeline model exhibiting lesser deviation between the automated measurement and the measurement made by the radiologist. The pipeline model however has better segmentation accuracy when the size of the image used for training is small. The convolutional neural network with auto encoder–decoder proves robust through sparse representation, and faster learning with better generalization. Also, the experimental setup is analyzed by interconnecting Tensor flow simulated result with Raspberry PI and the outcomes are analyzed.

Detecting Carotid Intima-Media From Small-Sample Ultrasound Images

Cardiovascular diseases are the biggest threat to human being's health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great significance to detect the intima-media (IM). In this paper, a new IM detection method based on convolution neural network (IMD-CNN) is proposed for the detection of IM of blood vessels in longitudinal ultrasonic images. In IMD-CNN, firstly the region of interest (ROI) is automatically extracted by morphological processing, then the patch-wise training data are constructed, and finally a simple CNN is trained to detect the IM. The experimental results obtained on 23 images show that the test accuracy of IMD-CNN is over 86% and the performance of IMD-CNN is also visually proved to be effective.