42 research outputs found
Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet
Catheter segmentation in 3D ultrasound is important for computer-assisted
cardiac intervention. However, a large amount of labeled images are required to
train a successful deep convolutional neural network (CNN) to segment the
catheter, which is expensive and time-consuming. In this paper, we propose a
novel catheter segmentation approach, which requests fewer annotations than the
supervised learning method, but nevertheless achieves better performance. Our
scheme considers a deep Q learning as the pre-localization step, which avoids
voxel-level annotation and which can efficiently localize the target catheter.
With the detected catheter, patch-based Dual-UNet is applied to segment the
catheter in 3D volumetric data. To train the Dual-UNet with limited labeled
images and leverage information of unlabeled images, we propose a novel
semi-supervised scheme, which exploits unlabeled images based on hybrid
constraints from predictions. Experiments show the proposed scheme achieves a
higher performance than state-of-the-art semi-supervised methods, while it
demonstrates that our method is able to learn from large-scale unlabeled
images.Comment: Accepted by MICCAI 202