Deep learning-based measurement of total plaque area in B-mode ultrasound images.

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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 (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 the 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) mm2 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 TPA=-0.444.05 mm2 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.