Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood clots. PE accounts for approximately 100,000 deaths per year in the United States alone. The clinical presentation of PE is often nonspecific, making the diagnosis challenging. Thus, rapid and accurate risk stratification is of paramount importance. High-risk PE is caused by right ventricular (RV) dysfunction from acute pressure overload, which in return can help identify which patients require more aggressive therapy. Reconstructed four-chamber views of the heart on chest CT can detect right ventricular enlargement. CT pulmonary angiography (CTPA) is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies. We developed a weakly supervised deep learning algorithm, with an emphasis on a novel attention mechanism, to automatically classify RV strain on CTPA. Our method is a 3D DenseNet model with integrated 3D residual attention blocks. We evaluated our model on a dataset of CTPAs of emergency department (ED) PE patients. This model achieved an area under the receiver operating characteristic curve (AUC) of 0.88 for classifying RV strain. The model showed a sensitivity of 87% and specificity of 83.7%. Our solution outperforms state-of-the-art 3D CNN networks. The proposed design allows for a fully automated network that can be trained easily in an end-to-end manner without requiring computationally intensive and time-consuming preprocessing or strenuous labeling of the data.We infer that unmarked CTPAs can be used for effective RV strain classification. This could be used as a second reader, alerting for high-risk PE patients. To the best of our knowledge, there are no previous deep learning-based studies that attempted to solve this problem.
Athena-Research and Innovation Center in Information, Communication and Knowledge Technologies（雅典娜信息、通信与知识技术研究与创新中心）；Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）
The main objective of this work is to utilize state-of-the-art deep learning approaches for the identification of pulmonary embolism in CTPA-Scans for COVID-19 patients, provide an initial assessment of their performance and, ultimately, provide a fast-track prototype solution (system). We adopted and assessed some of the most popular convolutional neural network architectures through transfer learning approaches, to strive to combine good model accuracy with fast training. Additionally, we exploited one of the most popular one-stage object detection models for the localization (through object…
University of California, Santa Barbara（加州大学圣巴巴拉分校）
Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heuristically cluster views for local fusion, thus restricting their ability to consider all views jointly. Our key insight is that greater detail can be retained without restricting view diversity by learning a view-fusion function conditioned on camera pose and image content. We pr…
Arizona State University（亚利桑那州立大学）；Mayo Clinic（梅奥诊所）
Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE. However, numerous competing methods for a given task in the deep learning literature exist, causing great confusion regarding the development of a CAD PE system. To address this confusion, we present a comprehensive analysis…
Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory（先进成像与放射治疗信息学（AIRT）实验室）；The Medical Artificial Intelligence and Automation (MAIA) Laboratory（医学人工智能与自动化（MAIA）实验室）；Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤科）；Department of Medical Physics, Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心医学物理系）
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院）
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Faculty of Engineering, Tel Aviv University（工程学院，特拉维夫大学）；Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai（数据驱动与数字医学部，医学部，Mount Sinai医学院）；Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel（诊断影像科，Sheba 医疗中心，Ramat Gan，以色列）；Department of Radiology, Icahn School of Medicine, Mount Sinai（放射科，Icahn医学院，Mount Sinai）
Infervision Medical Technology Co., Ltd.（推想医疗科技有限公司）；UCLA（加州大学洛杉矶分校）
In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to difficulties in medical diagnosis by either physicians or computer-aided diagnosis algorithms. Deep learning-based volumetric super-resolution (SR) methods are feasible ways to improve resolution, with convolutional neural networks (CNN) at their core. Despite recent progress, these methods are limited by inherent properties of convolution operators, which ignore content relevance an…
LMU München（慕尼黑大学）；Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally increasing model complexity. So far, the development of SE has focused on 2D images. In this paper, we propose `Project & Excite' (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images. `Project & Excite' does not perform global average po…
Khulna University of Engineering & Technology（库尔纳工程技术大学）；University Health Network（大学健康网络）；University of Toronto（多伦多大学）
The novel COVID-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity is imperative for disease diagnosis and prognosis as early as possible. It also can be a helpful tool in triage for testing and clinical supervision of COVID-19 patients. However, designing such an automated tool from non-invasive radiographic images is challenging as many manually annotated datasets are not publicly available yet, which is the essential core requirement of supervised learning schemes. This article proposes a 3D Convolutional Neural Network (CNN)-based…
Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between t…
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
University of Haifa（海法大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；Sackler Faculty of Medicine, Tel Aviv University（特拉维夫大学萨克勒医学院）；Tel Aviv University（特拉维夫大学）；Sagol Brain Institute（萨戈尔脑研究所）；Sagol School of Neuroscience, Tel Aviv University（特拉维夫大学萨戈尔神经科学学院）；School of Computer Science and Engineering, The Hebrew University of Jerusalem（希伯来大学计算机科学与工程学院）；The Hebrew University of Jerusalem（希伯来大学）；Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem（希伯来大学埃德蒙与莉莉·萨夫拉脑科学中心）
University of Brescia（布雷西亚大学）；University of Glasgow（格拉斯哥大学）
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the t…
College of Biomedical Engineering, Rangsit University（兰实大学生物医学工程学院）
Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. This paper describes a method developed in response to the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210 kidney cancer patients were used to develop and evaluate this automatic segmentation method based on a logical ensemble of fully-convolutional network (FCN) architectures, followed by volumetric validation. Data was pre-proce…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；Lenovo Research（联想研究院）
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN). The first stage preliminarily locate the kidney and cut off the irrelevant background to reduce class imbalance and computation cost. Then the second stage precisely segment the kidney and tumor on the cropped patch. The proposed method ranks the 4th place out of 105 competitive teams in MICCAI 2019 KiTS Challenge with a Comp…
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
Lausanne University Hospital (CHUV)（洛桑大学医院）；Siemens Healthineers International AG（西门子医疗国际股份公司）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Lausanne (UNIL)（洛桑大学）
University of Dayton（代顿大学）；St. Jude Children's Research Hospital（圣裘德儿童研究医院）；Microsoft（微软）
3D lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D…
Modern optical coherence tomography (OCT) devices provide volumetric images with micrometer-scale spatial resolution and a temporal resolution beyond video rate. In this work, we analyze an OCT-based prototypical tracking system which processes 831 volumes per second, estimates translational motion, and automatically adjusts the field-of-view, which has a size of few millimeters, to follow a sample even along larger distances. The adjustment is realized by two galvo mirrors and a motorized reference arm, such that no mechanical movement of the scanning setup is necessary. Without requiring a m…
We propose a novel solution for volumetric ultrasound imaging using single-side access 3-D synthetic-aperture scanning of a clinical linear array. This solution is based on an advanced scanning geometry and a software-based ultrasound platform. The rotate-translate scanning scheme increases the elevation angular aperture by pivoting the array [-45{\textdegree} to 45{\textdegree}] around its array axis (axis along the row of its elements) and then, scans the imaged object for each pivoted angle by translating the array perpendicularly to the rotation axis. A theoretical basis is presented so th…
