Automated Catheter Localization in Volumetric Ultrasound Using 3D Patch-Wise U-Net with Focal Loss

3D ultrasound (US) imaging has become an attractive option for image-guided interventions. Fast and accurate catheter localization in 3D cardiac US can improve the outcome and efficiency of the cardiac interventions. In this paper, we propose a catheter localization method for 3D cardiac US using th...

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Bibliographic Details
Main Authors: Yang, Hongxu, Shan, Caifeng, Kolen, Alexander F., N. de With, Peter H.
Format: Conference Proceeding
Language:English
Subjects:
3D U-Net
3D ultrasound
Catheter localization
Catheters
Dense sampling
Feature extraction
Focal loss
Image segmentation
Noise measurement
Solid modeling
Three-dimensional displays
Training
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Summary:3D ultrasound (US) imaging has become an attractive option for image-guided interventions. Fast and accurate catheter localization in 3D cardiac US can improve the outcome and efficiency of the cardiac interventions. In this paper, we propose a catheter localization method for 3D cardiac US using the patch-wise semantic segmentation with model fitting. Our 3D U-Net is trained with the focal loss of cross-entropy, which makes the network to focus more on samples that are difficult to classify. Moreover, we adopt a dense sampling strategy to overcome the extremely imbalanced catheter occupation in the 3D US data. Extensive experiments on our challenging ex-vivo dataset show that the proposed method achieves an F-1 score of 65.1% for catheter segmentation, outperforming the state-of-the-art methods. With this, our method can localize RF-ablation catheters with an average error of 1.28 mm.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803045