18 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
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research