Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8 % improvement when compared to state-of-the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.Comment: ISBI 2019 accepte

Meta-analysis of genome-wide association studies for extraversion:Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion

Improving catheter segmentation & location in 3D cardiac ultrasound using direction-fused fcn

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-of the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm

Catheter localization in 3D ultrasound using voxel-of-interest-based ConvNets for cardiac intervention

Purpose: Efficient image-based catheter localization in 3D US during cardiac interventions is highly desired, since it facilitates the operation procedure, reduces the patient risk and improves the outcome. Current image-based catheter localization methods are not efficient or accurate enough for real clinical use. Methods: We propose a catheter localization method for 3D cardiac ultrasound (US). The catheter candidate voxels are first pre-selected by the Frangi vesselness filter with adaptive thresholding, after which a triplanar-based ConvNet is applied to classify the remaining voxels as catheter or not. We propose a Share-ConvNet for 3D US, which reduces the computation complexity by sharing a single ConvNet for all orthogonal slices. To boost the performance of ConvNet, we also employ two-stage training with weighted cross-entropy. Using the classified voxels, the catheter is localized by a model fitting algorithm. Results: To validate our method, we have collected challenging ex vivo datasets. Extensive experiments show that the proposed method outperforms state-of-the-art methods and can localize the catheter with an average error of 2.1 mm in around 10 s per volume. Conclusion: Our method can automatically localize the cardiac catheter in challenging 3D cardiac US images. The efficiency and accuracy localization of the proposed method are considered promising for catheter detection and localization during clinical interventions

Improving catheter segmentation & location in 3D cardiac ultrasound using direction-fused fcn

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-of the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm

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 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

Efficient catheter segmentation in 3D cardiac ultrasound using slice-based FCN with deep supervision and F-score loss

Fast and accurate catheter segmentation in 3D ultrasound (US) can improve the outcome and efficiency of cardiac interventions. In this paper, we propose an efficient catheter segmentation method based on a fully convolutional neural network (FCN). The FCN is based on a pre-trained VGG-16 model, which processes the 3D US volumes slice by slice. To enhance its performance, we modify its structure by skipping connections under a deep supervision structure, which is learned with an F-score loss function. Our method can exploit more contextual information and increase the detection of catheter-like voxels. We collected a challenging ex-vivo dataset (92 3D US images) from porcine hearts with an RF-ablation catheter inside. Our experiments on this dataset show that the proposed method achieves a segmentation performance with an F 2 score of 65.2% with a highly efficient inference around 1.1 sec. per volume

Catheter detection in 3D ultrasound using triplanar-based convolutional neural networks

3D Ultrasound (US) image-based catheter detection can potentially decrease the cost on extra equipment and training. Meanwhile, accurate catheter detection enables to decrease the operation duration and improves its outcome. In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanar-based CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. When compared to state-of-the-art handcrafted features on ex-vivo datasets, our proposed method improves the F2-score with at least 31%. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images