Left atrial appendage segmentation from 3D CCTA images for occluder placement procedure

Leventić, Hrvoje; Babin, Danilo; Velicki, Lazar; Devos, Daniël; Galić, Irena; Zlokolica, Vladimir; Romić, Krešimir; Pižurica, Aleksandra

doi:10.1016/j.compbiomed.2018.11.006

Cited by 28 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the measurement algorithms, not only the time of the measurement can be greatly reduced, but also the reproducibility will be improved. In this regard, studies that focus on the auto or semi-auto measurement, like Leventic et al's ( 125 ) work, should be encouraged in future studies. Clinical studies are also needed to validate the developed algorithms.…”

Section: Discussionmentioning

confidence: 99%

Stroke risk evaluation for patients with atrial fibrillation: Insights from left atrial appendage

Fang

Wang

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Left atrial appendage (LAA) is believed to be a common site of thrombus formation in patients with atrial fibrillation (AF). However, the commonly-applied stroke risk stratification model (such as. CHA2DS2-VASc score) does not include any structural or hemodynamic features of LAA. Recent studies have suggested that it is important to incorporate LAA geometrical and hemodynamic features to evaluate the risk of thrombus formation in LAA, which may better delineate the AF patients for anticoagulant administration and prevent strokes. This review focuses on the LAA-related factors that may be associated with thrombus formation and cardioembolic events.

show abstract

Section: Discussionmentioning

confidence: 99%

Stroke risk evaluation for patients with atrial fibrillation: Insights from left atrial appendage

Fang

Wang

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Datasets often contain low number of images and ground truth segmentations are created by inhouse medical experts. However, several papers [16,18] have shown non-negligible subjectivity between the ground truth segmentations from different medical experts. The approaches that quantify and highlight this subjectivity are mostly heuristics-based, since these approaches do not require definite ground truth segmentations for training.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, most of the methods are semi-automatic and require user interaction. Some methods require one or more user-selected seed points [24,16], thresholds [16], or a user provided centerline through the LAA [18].…”

Section: Heuristics-based Segmentation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images

Leventić¹,

Benčević²,

Babin³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Atrial fibrillation (AF) is a cardiovascular disease identified as one of the main risk factors for stroke. The majority of strokes due to AF are caused by clots originating in the left atrial appendage (LAA). LAA occlusion is an effective procedure for reducing stroke risk. Planning the procedure using pre-procedural imaging and analysis has shown benefits. The analysis is commonly done by manually segmenting the appendage on 2D slices. Automatic LAA segmentation methods could save an expert's time and provide insightful 3D visualizations and accurate automatic measurements to aid in medical procedures. Several semi-and fully-automatic methods for segmenting the appendage have been proposed. This paper provides a review of automatic LAA segmentation methods on 3D and 4D medical images, including CT, MRI and echocardiogram images. We classify methods into heuristic and modelbased methods, as well as into semi-and fully-automatic methods. We summarize and compare the proposed methods, evaluate their effectiveness, present current challenges in the field and approaches to overcome them.

show abstract

“…More importantly, it is subjective and prone to variations in observers' expertise and knowledge. To overcome this issue, the various semi-automatic [2] and automatic approaches [3] for cardiac segmentation using different image processing and machine learning techniques are developing rapidly. In the beginnings of cardiac research, the methods based on partial differential equations, where internal or external evolving curves are differentiating heart region from other non-interest parts, are majorly investigated [4]- [8].…”

Section: Related Workmentioning

confidence: 99%

Neural Network based Whole Heart Segmentation from 3D CT images

Habijan¹,

Leventić²,

Galić³

et al. 2020

Int. j. electr. comput. eng. syst. (Online)

Self Cite

View full text Add to dashboard Cite

The most recent research is showing the importance and suitability of neural networks for medical image processing tasks. Nonetheless, their efficiency in segmentation tasks is greatly dependent on the amount of available training data. To overcome issues of using small datasets, various data augmentation techniques have been developed. In this paper, an approach for the whole heart segmentation based on the convolutional neural network, specifically on the 3D U-Net architecture, is presented. Also, we propose the incorporation of the principal component analysis as an additional data augmentation technique. The network is trained end-to-end, i.e., no pre-trained network is required. Evaluation of the proposed approach is performed on CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, delivering in a three-fold cross-validation an average dice coefficient overlap of 88.2% for the whole heart, i.e. all heart substructures. Final segmentation results show a high accuracy with the ground truth, indicating that the proposed approach is competitive to the state-of-the-art. Additionally, experiments on the influence of different learning rates are provided as well, showing the optimal learning rate of 0.005 to give the best segmentation results.

show abstract

Left atrial appendage segmentation from 3D CCTA images for occluder placement procedure

Cited by 28 publications

References 26 publications

Stroke risk evaluation for patients with atrial fibrillation: Insights from left atrial appendage

Stroke risk evaluation for patients with atrial fibrillation: Insights from left atrial appendage

A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images

Neural Network based Whole Heart Segmentation from 3D CT images

Contact Info

Product

Resources

About