1 research outputs found
3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications
Ultrasound (US) speckles are granular patterns which can impede image
post-processing tasks, such as image segmentation and registration.
Conventional filtering approaches are commonly used to remove US speckles,
while their main drawback is long run-time in a 3D scenario. Although a few
studies were conducted to remove 2D US speckles using deep learning, to our
knowledge, there is no study to perform speckle reduction of 3D B-mode US using
deep learning. In this study, we propose a 3D dense U-Net model to process 3D
US B-mode data from a clinical US system. The model's results were applied to
3D registration. We show that our deep learning framework can obtain similar
suppression and mean preservation index (1.066) on speckle reduction when
compared to conventional filtering approaches (0.978), while reducing the
runtime by two orders of magnitude. Moreover, it is found that the speckle
reduction using our deep learning model contributes to improving the 3D
registration performance. The mean square error of 3D registration on 3D data
using 3D U-Net speckle reduction is reduced by half compared to that with
speckles.Comment: 10 pages, 3 figures and 3 table