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Lumen and vessel wall segmentation on intravascular ultrasound images using fully convolutional network
Author(s): Jiyeon Ko; June-Goo Lee
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Paper Abstract

In this study, we performed deep learning analysis for the automatic segmentation of vessel and lumen in intravascular ultrasound (IVUS) images. Extracting vascular boundaries from intravascular ultrasound images are essential for the quantitative analysis of cardiovascular diseases. We applied a fully convolutional network (FCN) based semantic segmentation technique and transfer learning. To consider the continuity of the IVUS images, we filled in RGB channels with the central image and the nearby images with displacement and trained different FCN model for each displacement. Based on our experiments, we obtained 0.97 ± 0.03 of dice similarity coefficient (DSC) value in the vessel and 0.91 ± 0.09 of DSC value in the lumen. Due to their robustness and accuracy, this method is highly promising to be used in clinical practice.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500R (27 March 2019); doi: 10.1117/12.2521363
Show Author Affiliations
Jiyeon Ko, Asan Medical Ctr. (Korea, Republic of)
June-Goo Lee, Asan Medical Ctr. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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