Abstract
| Language | English |
|---|---|
| Title of host publication | Image-Guided Procedures, Robotic Interventions, and Modeling |
| Subtitle of host publication | Image-Guided Procedures, Robotic Interventions, and Modeling |
| State | Accepted/In press - 2020 |
| Event | 2020 SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States Duration: 16 Feb 2020 → 19 Feb 2020 |
Conference
| Conference | 2020 SPIE Medical Imaging |
|---|---|
| Country | United States |
| City | Houston |
| Period | 16/02/20 → 19/02/20 |
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Feasibility study of catheter segmentation in 3D Frustum Ultrasounds by DCNN. / Min, Lan; Yang, Hongxu; Shan, Caifeng; Kolen, Alexander F.; de With, Peter.
Image-Guided Procedures, Robotic Interventions, and Modeling: Image-Guided Procedures, Robotic Interventions, and Modeling. 2020.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Feasibility study of catheter segmentation in 3D Frustum Ultrasounds by DCNN
AU - Min,Lan
AU - Yang,Hongxu
AU - Shan,Caifeng
AU - Kolen,Alexander F.
AU - de With,Peter
PY - 2020
Y1 - 2020
N2 - 3D ultrasound has been developed rapidly in medical intervention therapies, such as cardiac catheterization. Image-based catheter detection is studied to help sonographer to timely localize the instrument in the 3D US images. Conventionally, the 3D imaging methods are based on the Cartesian domain, which is limited by bandwidth and information lose when it is converted from the original acquisition space---Frustum domain. The catheter segmentation in the Frustum space helps to reduce the computational cost and improve efficiency. In this paper, we present a catheter segmentation method in 3D Frustum image via a deep convolutional network (DCNN). To accelerate the prediction efficiency on whole US Frustum volume, a filter-based pre-selection is applied to reduce the computational cost of the DCNN. Based on experiments on the ex-vivo dataset, our proposed method can segment the catheter in Frustum images with 0.67 Dice score within 3 seconds.
AB - 3D ultrasound has been developed rapidly in medical intervention therapies, such as cardiac catheterization. Image-based catheter detection is studied to help sonographer to timely localize the instrument in the 3D US images. Conventionally, the 3D imaging methods are based on the Cartesian domain, which is limited by bandwidth and information lose when it is converted from the original acquisition space---Frustum domain. The catheter segmentation in the Frustum space helps to reduce the computational cost and improve efficiency. In this paper, we present a catheter segmentation method in 3D Frustum image via a deep convolutional network (DCNN). To accelerate the prediction efficiency on whole US Frustum volume, a filter-based pre-selection is applied to reduce the computational cost of the DCNN. Based on experiments on the ex-vivo dataset, our proposed method can segment the catheter in Frustum images with 0.67 Dice score within 3 seconds.
M3 - Conference contribution
BT - Image-Guided Procedures, Robotic Interventions, and Modeling
ER -