Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

Chen

Future Generation Computer Systems

et al. 2020

Self Cite

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3DLGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

Section: Segmentation Of the La Anatomymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Chen

Future Generation Computer Systems

et al. 2020

Self Cite

“…More accurate delineation could be achieved via information gained from the MRA, but the method also suffered from the additional registration error. Yang et al [21] utilized multi-atlas based whole heart segmentation to solve the problem and proposed a super-voxel based post-processing to achieve more accurate segmentation of the PV. Recently, deep learning based methods have attracted lots of interests mainly due to their performance and efficiency by leveraging the available big data and GPU computing.…”

Section: Multiview Sequential Learning and Dilated Residual Learning mentioning

confidence: 99%

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Zhang

et al. 2018

Self Cite

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients. I. INTRODUCTION Noninvasive MRI is becoming an increasingly important tool for medical diagnosis and is widely used in many diverse clinical applications, e.g., grading and classifying brain tumours [1], [2] and evaluating volumes and function of the

“…Moreover, in the AF patient population, prolonged scanning time, irregular breathing pattern and heart rate variability during the scan can result in poor image quality that can further complicate both segmentation tasks. Because of this, previous studies have segmented the LA and PV anatomy from an additional bright-blood data acquisition, and have then registered the segmented LA and PV anatomy to the LGE-CMRI acquisition for visualisation and delineation of the atrial scars [4,5,6]. This approach is complicated by motion (bulk, respiratory or cardiac) between the two acquisitions and subsequent registration errors.…”

mentioning

confidence: 99%

Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

et al. 2018

Self Cite

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.