Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

Figures

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  Figure 1. The process flow diagram of the presented algorithm. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 2. The flow chat of MAB segmentation including the process of training stage and testing stage. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 3. The architecture of our pretraining convolutional neural network (CNN), which contains two convolutional‐pooling blocks and two full connection layers. The inputs of the CNN are 64 × 64 patches generated along norm lines of the initial contours. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 4. The initialization of MAB segmentation. (a) User interaction on transverse slices of three‐dimensional ultrasound common carotid artery images every 3 mm (i.e., every three slices). The red points are located manually and the blue contours are generated using cubic spline interpolation. (b) Generated initial contours of all slices in (a). (c) Interpolated initial MAB contours from the corresponding points of the two adjacent user interaction slices in (b). [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 5. Generate patches along norm line of each initial MAB. The red contour is the initial contour, the yellow line is the generated norm line and the blue rectangle is a patch extracted along the norm line. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 6. The modified U‐Net architecture. Blue boxes represent feature maps. The number of channels is denoted above each feature map. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 7. The architecture of convolutional block with a shortcut

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  Figure 8. A comparison between the performance of algorithm and manual MAB and lumen‐intima boundary segmentations. The manual segmentations were generated at an interslice distance of 1 mm. The yellow contours show the manual segmentations and the red contours are the algorithm segmentations. [Color figure can be viewed at http://wileyonlinelibrary.com]

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  Figure 9. (a) Correlation plot for algorithm‐ and manually generated three‐dimensional ultrasound vessel‐wall‐volume (VWV). (b) Bland–Altman plot of the difference between VWVs generated by algorithm and manual segmentations. The red continuous line labeled as mean indicates the bias, the dotted lines labeled as 1.96 SD indicate the level of agreement. [Color figure can be viewed at http://wileyonlinelibrary.com]