Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets

Figures

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  Figure 1. Workflow for the automatic processing of the lumen boundary in intravascular ultrasound (IVUS) datasets. (A) Raw IVUS dataset (longitudinal view); (B) combined signal to describe the motion contained in the IVUS images and select the end-diastolic frames; (C) gated volume constructed from the selected end-diastolic frames; (D) multi-frame convolutional neural network (MFCNN) architecture; (E) mapping from image to contour domain through Gaussian Process (GP) regression; (F) transversal and longitudinal lumen outline on top of the gated dataset; (G) final lumen boundary represented in three-dimensional (3D) space as a surface.

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  Figure 2. Segmentation error for the different machine learning (ML) models. (A) Lumen contour in Cartesian (top) and polar (bottom) coordinates illustrating the mismatch between ground truth (GT) and ML segmentations; (B) definition of relevant metrics (relative distance error, maximum distance error, and degree of overlapping) to assess the performance of the ML segmentation; (C) notation for the GT and ML models; (D) violin plot of the relative distance error for the entire test set; (E) violin plot of the maximum distance error for the entire test set; (F) violin plot of the degree of overlapping for the entire test set; (G) violin plots showing the relative distance error metric at a per-pullback level. In all plots, horizontal bars indicate scenarios with statistically different results.

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  Figure 3. Lumen area error for the different machine learning (ML) models. (A) Lumen contour in Cartesian coordinates illustrating the definition of ground truth (GT) lumen area and ML lumen area; (B) definition of the metrics to assess the error in the lumen area; (C) violin plot of the relative lumen area error for different ML models (multi-frame—MF1, MF11, and MF11-Gaussian Process—GP) and the entire test dataset; (D) correlation plot between the ML lumen area (MF11-GP) and the GT lumen area; (E) Bland–Altman plot for the signed lumen area error as a function of the GT lumen area.

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  Figure 4. Randomly selected frames showing a raw image, ground truth (GT) lumen boundary (green contour), and machine learning (ML) lumen boundary (multi-frame—MF11-Gaussian process—GP, red contour).