Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

Karim, Rashed; Blake, Lauren-Emma; Inoue, Jiro; Tao, Qian; Jia, Shuman; Housden, James; Bhagirath, Pranav; Duval, Jean-Luc; Varela, Marta; Behar, Jonathan M.; Cadour, Loïc; Geest, Rob J. van der; Cochet, Hubert; Drangova, Maria; Sermesant, Maxime; Razavi, Reza; Aslanidi, Oleg; Rajani, Ronak; Rhode, Kawal

doi:10.1016/j.media.2018.08.004

Cited by 57 publications

(76 citation statements)

References 52 publications

(72 reference statements)

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“…Secondly, the quantification of scars in our work is performed on the surface mesh projected from the LA endocardium. Karim et al (2018) discussed the importance of wall thickness, particularly considering the potential that the ectopic activity can prevail in scars that are non-transmural. However, they also emphasized that the relationship between the AF and the changes in wall thickness was not clear, and the thickness was difficult to measure based on current MRI data.…”

Section: Discussionmentioning

confidence: 99%

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Yang

et al. 2020

Medical Image Analysis

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Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have employed fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be useful in diagnosis and prognosis of AF.

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Section: Discussionmentioning

confidence: 99%

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Yang

et al. 2020

Medical Image Analysis

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“…This is likely due to the limited relevant public datasets as well as the difficulty of the task. In addition, to the best of our knowledge, there are very few works that apply deep learning techniques to atrial wall segmentation, as also suggested by a recent survey paper (Karim et al, 2018). In the following sections, we will describe and discuss these methods regarding different applications in detail.…”

Section: Cardiac Mr Image Segmentationmentioning

confidence: 99%

Deep Learning for Cardiac Image Segmentation: A Review

Chen

Qiu

et al. 2020

Front. Cardiovasc. Med.

648

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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.

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“…On comparing the computational speed for different multi-atlas methods for automatic CTA segmentation, whereas some groups [8], [9], [14], [17], [29] did not report the computational time, those groups that did [5]- [7], [10], [12], [15], [18] required a much longer processing time (greater than five minutes) than our method (less than three minutes). The processing voxel size is unsurprisingly one of the key factors influencing the overall segmentation time.…”

Section: Discussionmentioning

confidence: 79%

“…For the WH segmentation, Funka-Lea et al [25] proposed a graph-cuts method, Zheng et al [26] presented a marginal space learning method, and van Rikxoort et al [5] proposed a multiatlas based segmentation technique. Likewise, Jolly [27] proposed a graph-cuts and EM-based method to segment the LV, Karim et al [17] segmented the LA, Yang et al [8] segmented the LV and LVM, and Tobon et al [9] segmented the LA and PA only.…”

Section: Previous Workmentioning

confidence: 99%

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

et al. 2020

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Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application. INDEX TERMS Computed tomography, heart segmentation, multi-atlas segmentation, random walk.

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Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database

Cited by 57 publications

References 52 publications

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Deep Learning for Cardiac Image Segmentation: A Review

Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography

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