  Intravascular ultrasound (IVUS) imaging allows direct visualization of the
coronary vessel wall and is suitable for the assessment of atherosclerosis and
the degree of stenosis. Accurate segmentation and measurements of lumen and
median-adventitia (MA) from IVUS are essential for such a successful clinical
evaluation. However, current segmentation relies on manual operations, which is
time-consuming and user-dependent. In this paper, we aim to develop a deep
learning-based method using an encoder-decoder deep architecture to
automatically extract both lumen and MA border. Our method named IVUS-U-Net++
is an extension of the well-known U-Net++ model. More specifically, a feature
pyramid network was added to the U-Net++ model, enabling the utilization of
feature maps at different scales. As a result, the accuracy of the probability
map and subsequent segmentation have been improved We collected 1746 IVUS
images from 18 patients in this study. The whole dataset was split into a
training dataset (1572 images) for the 10-fold cross-validation and a test
dataset (174 images) for evaluating the performance of models. Our IVUS-U-Net++
segmentation model achieved a Jaccard measure (JM) of 0.9412, a Hausdorff
distance (HD) of 0.0639 mm for the lumen border, and a JM of 0.9509, an HD of
0.0867 mm for the MA border, respectively. Moreover, the Pearson correlation
and Bland-Altman analyses were performed to evaluate the correlations of 12
clinical parameters measured from our segmentation results and the ground
truth, and automatic measurements agreed well with those from the ground truth
(all Ps<0.01). In conclusion, our preliminary results demonstrate that the
proposed IVUS-U-Net++ model has great promise for clinical use.
