Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Image Sciences Institute, University Medical Center Utrecht, Utrecht University（乌得勒支大学医学中心影像科学研究所，乌得勒支大学）；Diagnostic Image Analysis Group at the Department of Radiology and Nuclear Medicine, Radboud University Medical Center（拉德堡德大学医学中心放射学与核医学系诊断影像分析组）；Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所）；Department of Radiology, University Medical Center Utrecht, Utrecht University（乌得勒支大学医学中心放射科，乌得勒支大学）
Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum
发表信息：
IEEE Transactions on Medical Imaging 37(2), pp 615-625, 2018
Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.
Cardiovascular disease (CVD) is a common and strong threat to human beings, featuring high prevalence, disability and mortality. The amount of coronary artery calcification (CAC) is an effective factor for CVD risk evaluation. Conventionally, CAC is quantified using ECG-synchronized cardiac CT but rarely from general chest CT scans. However, compared with ECG-synchronized cardiac CT, chest CT is more prevalent and economical in clinical practice. To address this, we propose an automatic method based on Dense U-Net to segment coronary calcium pixels on both types of CT scans. Our contribution i…
University Medical Center Utrecht and Utrecht University（乌得勒支大学医学中心与乌得勒支大学）
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registrati…
University of Chittagong（吉大港大学）；Charles Sturt University（查尔斯特大学）
The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient's chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray…
ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT
Department of Radiology, Northwestern University, Chicago, IL, USA；College of Computing, Georgia Institute of Technology, Atlanta, GA, USA；Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA；Department of Cardiology, Northwestern University, Chicago, IL, USA；Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels.…
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）
Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131X131 voxels, that classified voxels in axial,…
Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）；Utrecht University（乌得勒支大学）；University of Amsterdam（阿姆斯特丹大学）
Purpose: Coronary artery calcium (CAC) score, i.e. the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-ECG-synchronized CT where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a thre…
Carnegie Mellon University（卡内基梅隆大学）；Case Western Reserve University（凯斯西储大学）
Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view. However, acquisition of DE images requires two physical scans, necessitating specialized hardware and processing, and images are prone to motion artifact. Generation of virtual DE images from standard, single-shot chest radiographs would expand the diagnostic value of standard radiographs without changing the acquisition procedure. We present a Multi-scale Conditional Adve…
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology Delhi（印度理工学院德里分校巴蒂电信技术与管理学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College, Vellore（韦洛尔基督教医学院）
A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases, such as lung cancer, tuberculosis, pneumonia, and many more. Automated segmentation of the lungs is an important step to design a computer-aided diagnostic tool for examination of a CxR. Precise lung segmentation is considered extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR. We attempt an…
A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography
Center for Applied AI, University of Kentucky（应用人工智能中心，肯塔基大学）；EduceLab, University of Kentucky（EduceLab，肯塔基大学）；Department of Medicine, Division of Cardiovascular Medicine and the Gill Heart and Vascular Institute, University of Kentucky（医学部，心血管医学分会和吉尔心脏和血管研究所，肯塔基大学）；Department of Radiology, College of Medicine, University of Kentucky（放射学部，医学院，肯塔基大学）
Florida International University（佛罗里达国际大学）；Technical University of Munich（慕尼黑工业大学）；Stanford University（斯坦福大学）
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously t…
Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require the searching of calcification on these scans. Only if someone is suspected to have a heart problem, a gated CT scan is taken, otherwise, there's no way for the patient to be aware of a possible heart attack/disease. While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries. In fact, in real ti…
Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network
Department of Medical Imaging, University of Saskatchewan（医学成像系，萨斯喀彻温大学）；Department of Electrical and Computer Engineering, University of Saskatchewan（电气与计算机工程系，萨斯喀彻温大学）
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Città della Salute e della Scienza Hospital（健康与科学城医院）；University of Turin（都灵大学）；Civil Hospital（平民医院）；Candiolo Cancer Institute, FPO-IRCCS（坎迪奥洛癌症研究所，FPO-IRCCS）；Ospedale San Giovanni Bosco（圣乔瓦尼·博斯科医院）
Artificial Intelligence-Based Opportunistic Coronary Calcium Screening in the Veterans Affairs National Healthcare System
Veterans Affairs Long Beach Healthcare System（退伍军人事务部长滩医疗系统）；University of California, Irvine（加州大学欧文分校）；Veterans Health Administration（退伍军人健康管理局）；University of Colorado School of Public Health（科罗拉多大学公共卫生学院）；Veterans Affairs Kansas City Healthcare System（退伍军人事务部堪萨斯城医疗系统）；Veterans Affairs San Diego Healthcare System（退伍军人事务部圣迭戈医疗系统）；University of California, San Diego（加州大学圣迭戈分校）；Mass General Brigham（麻省总医院布莱根医疗系统）；Harvard Medical School（哈佛医学院）；Brigham and Women's Hospital（布莱根妇女医院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
University of Saskatchewan（萨斯喀彻温大学）；Chonnam National University Hwasun Hospital（全南大学和顺医院）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）；National Institute of Technology, Trichy（特里吉国家理工学院）；Jeonbuk National University Medical School（全北国立大学医学院）
