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

Abstract

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.

Keywords: Attention-UNet; CCA; CVD; ICA; UNet; UNet++; UNet+++; atherosclerosis; deep learning; plaque segmentation; stroke.

Figure 1

Global system (under the class of AtheroEdge™ 3.0) of carotid plaque segmentation.

Figure 2

Basic Unet model with four encoder and decoder stages.

Figure 3

Unet++ model with four encoder and decoder stages.

Figure 4

Unet3P model with four encoder and decoder stages.

Figure 5

Attention block with input features, gating signals and weighted output.

Figure 6

The attention gate mechanism between the first encoder and decoder layer.

Figure 7

Four-stage Attention-UNet model.

Figure 8

Visual results of the segmentation of Japanese, Hong Kong and UK (ICA) databases, performed by UNet, UNet++, UNet3P, and Attention-UNet models.

Figure 9

Attention channel effect on plaque segmentation on critical images of moderate and high plaque.

Figure 10

Regression plots for all 6 types of UNet models for ICA DB1 databases.

Figure 11

Regression Plot for all 6 types of UNet models for CCA DB2A database.

Figure 12

ROC curves for all 6 types of UNet model for ICA DB1 database.

Figure 13

ROC Plot for all 6 types of UNet models for CCA DB2A database.

Figure 14

Paired samples t-test Plot for all 6 types of UNet models for ICA DB1 databases.

Figure 15

Paired samples t-test Plot for all 6 types of UNet models for CCA DB2A database.

Figure 16

Bland–Altman Plot of all 6 types of models for ICA DB1 database.

Figure 17

Bland–Altman Plot of all 6 types of models for CCA DB2A database.