@inproceedings{7ef52ecfc5eb4274a5e1ac4801139cfa,
title = "Feasibility study of catheter segmentation in 3D Frustum Ultrasounds by DCNN",
abstract = "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.",
keywords = "3D Frustum ultrasound, Catheter segmentation, DCNN, Ex-vivo dataset",
author = "Lan Min and Hongxu Yang and Caifeng Shan and Kolen, {Alexander F.} and {de With}, Peter",
year = "2020",
month = mar,
day = "16",
doi = "10.1117/12.2549084",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2020",
address = "United States",
note = "2020 SPIE Medical Imaging : Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 16-02-2020 Through 19-02-2020",
}