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

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Ran Zhou, Fumin Guo, M. Reza Azarpazhooh, Samineh Hashemi, Xinyao Cheng, John David Spence, Mingyue Ding, Aaron Fenster

Published: 01 Jan 2021, Last Modified: 07 Nov 2025IEEE J. Biomed. Health Informatics 2021EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ($\Delta$TPA), Pearson correlation coefficient (r) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3–85.7%, and algorithm TPAs were strongly correlated (r = 0.985–0.988; p < 0.001) with manual results with marginal biases (0.73–6.75) mm$^2$ using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (r = 0.972, p < 0.001) with $\Delta$TPA = −0.44$\pm$4.05 mm$^2$ and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.