  Accurate and efficient catheter segmentation in 3D ultrasound (US) is
essential for cardiac intervention. Currently, the state-of-the-art
segmentation algorithms are based on convolutional neural networks (CNNs),
which achieved remarkable performances in a standard Cartesian volumetric data.
Nevertheless, these approaches suffer the challenges of low efficiency and GPU
unfriendly image size. Therefore, such difficulties and expensive hardware
requirements become a bottleneck to build accurate and efficient segmentation
models for real clinical application. In this paper, we propose a novel Frustum
ultrasound based catheter segmentation method. Specifically, Frustum ultrasound
is a polar coordinate based image, which includes same information of standard
Cartesian image but has much smaller size, which overcomes the bottleneck of
efficiency than conventional Cartesian images. Nevertheless, the irregular and
deformed Frustum images lead to more efforts for accurate voxel-level
annotation. To address this limitation, a weakly supervised learning framework
is proposed, which only needs 3D bounding box annotations overlaying the
region-of-interest to training the CNNs. Although the bounding box annotation
includes noise and inaccurate annotation to mislead to model, it is addressed
by the proposed pseudo label generated scheme. The labels of training voxels
are generated by incorporating class activation maps with line filtering, which
is iteratively updated during the training. Our experimental results show the
proposed method achieved the state-of-the-art performance with an efficiency of
0.25 second per volume. More crucially, the Frustum image segmentation provides
a much faster and cheaper solution for segmentation in 3D US image, which meet
the demands of clinical applications.
