HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment
Kahlert School of Computing, University of Utah（犹他大学卡尔特计算机学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Johns Hopkins（约翰斯·霍普金斯大学）；Division of Cardiology, University of Utah（犹他大学心脏病学系）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与影像研究所）
K M Arefeen Sultan, Md Hasibul Husain Hisham, Benjamin Orkild, Alan Morris, Eugene Kholmovski, Erik Bieging, Eugene Kwan, Ravi Ranjan, Ed DiBella, Shireen Elhabian
The accurate evaluation of left atrial fibrosis via high-quality 3D Late Gadolinium Enhancement (LGE) MRI is crucial for atrial fibrillation management but is hindered by factors like patient movement and imaging variability. The pursuit of automated LGE MRI quality assessment is critical for enhancing diagnostic accuracy, standardizing evaluations, and improving patient outcomes. The deep learning models aimed at automating this process face significant challenges due to the scarcity of expert annotations, high computational costs, and the need to capture subtle diagnostic details in highly variable images. This study introduces HAMIL-QA, a multiple instance learning (MIL) framework, designed to overcome these obstacles. HAMIL-QA employs a hierarchical bag and sub-bag structure that allows for targeted analysis within sub-bags and aggregates insights at the volume level. This hierarchical MIL approach reduces reliance on extensive annotations, lessens computational load, and ensures clinically relevant quality predictions by focusing on diagnostically critical image features. Our experiments show that HAMIL-QA surpasses existing MIL methods and traditional supervised approaches in accuracy, AUROC, and F1-Score on an LGE MRI scan dataset, demonstrating its potential as a scalable solution for LGE MRI quality assessment automation. The code is available at: $\href{https://github.com/arf111/HAMIL-QA}{\text{this https URL}}$
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and…
Royal Brompton Hospital（皇家布朗普顿医院）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of Lincoln（林肯大学）；City University London（伦敦城市大学）
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is correlated with increased morbidity and mortality. It is associated with atrial fibrosis, which may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients. Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas bas…
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Department of Biomedical Engineering, Johns Hopkins（约翰斯·霍普金斯大学生物医学工程系）；Division of Cardiology, University of Utah（犹他大学心脏病学系）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Kahlert School of Computing, University of Utah（犹他大学卡勒特计算学院）；Department of Biomedical Engineering, University of Utah（犹他大学生物医学工程系）；Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah（犹他大学诺拉·埃克尔斯·哈里森心血管研究与培训研究所）；Department of Radiology and Imaging Sciences, University of Utah（犹他大学放射学与影像科学系）；Department of Biomedical Engineering, Johns Hopkins（约翰霍普金斯大学生物医学工程系）；Division of Cardiology, University of Utah（犹他大学心脏病学系）
Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa（放射科、放射肿瘤学与医学物理系，渥太华大学）；Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute（心内科，医学系，渥太华心脏研究所）
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However, automatic LA segmentation from LGE MRI is still challenging, due to the poor image quality, high variability in LA shapes, and unclear LA boundary. Though deep learning-based methods can provide promising LA segmentation results, they often generalize poorly to unseen domains, such as data from different scanners and/or sites. In this work, we collect 210 LGE MRIs from different centers with different levels of ima…
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooli…
Anhui University（安徽大学）；Royal Brompton Hospital（皇家布朗普顿医院）；National Heart & Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University College London（伦敦大学学院）；Alan Turing Institute（艾伦·图灵研究所）；Imperial College London（帝国理工学院）
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 superv…
Sun Yat-sen University（中山大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（帝国理工学院）
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates a…
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing …
Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques
University of Auckland（奥克兰大学）；Auckland Bioengineering Institute（奥克兰生物工程研究所）
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）；Technical University Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation therapy and predict treatment results for atrial fibrillation (AF) patients. The automatic segmentation is however still challenging, due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely Atr…
Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Anhui University（安徽大学）；Western University（西安大略大学）；University College London（伦敦大学学院）；Alan Turing Institute（阿兰·图灵研究所）；NIHR Imperial Biomedical Research Centre（NIHR帝国生物医学研究中心）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；School of Biomedical Engineering, Sun Yat-Sen University（中山大学医学院生物医学工程学院）
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 sub…
LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation
Gottfried Schatz Research Center: Medical Physics and Biophysics（戈特弗里德·施蔡茨研究所以及生物物理学）；Medical University of Graz（格拉茨医科大学）；Institute of Computer Graphics and Vision（计算机图形学与视觉研究所）；Graz University of Technology（格拉茨理工大学）；AVL List GmbH；Institute for Medical Informatics, Statistics and Documentation（医学信息学、统计学与文档研究所）
