Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary for the objective assessment of the lung diseases. In this paper, we develop semantic segmentation model for 5 kinds of DLDs. DLDs considered in this work are consolidation, ground glass opacity, honeycombing, emphysema, and normal. Convolutional neural network (CNN) is one of the most promising technique for semantic segmentation among machine learning algorithms. While creating annotated dataset for semantic segmentation is laborious and time consuming, creating partially annotated dataset, in which only one chosen class is annotated for each image, is easier since annotators only need to focus on one class at a time during the annotation task. In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset. The experiments using partially annotated dataset composed 372 CT images demonstrated that our proposed technique significantly improved segmentation accuracy.
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
QIMR Berghofer Medical Research Institute（昆士兰医学研究 institute）；Australian Institute for Bioengineering and Nanotechnology（澳大利亚生物工程与纳米技术研究所）；The University of Queensland（昆士兰大学）；Frazer Institute（弗拉泽研究所）；Princess Alexandra Hospital（亚历山大公主医院）；Faculty of Health, Medicine and Behavioural Sciences（健康、医学与行为科学学院）；School of Biomedical Sciences（生物医学科学学院）；Uppsala University（乌普萨拉大学）；Rohtak Nuclear Medcare Imaging Therapy and Research Center（罗塔克核医学成像治疗与研究中心）
NIHR Imperial Biomedical Research Centre（英国国家健康研究所帝国生物医学研究中心）；Imperial College London（帝国理工学院）；NHLI（英国国家心肺研究所）；Columbia University（哥伦比亚大学）；Royal Brompton and Harefield NHS Foundation Trust（皇家布朗普顿与哈菲尔德国民保健服务基金会信托）
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate follow-up challenging. We propose a novel method to train Convolutional Neural Networks using only regional labels on the presence of pathological signs, to not only detect CPA, but also spatially localize pathological signs. We use average intensity projections within different ranges of Hounsfield-unit (HU) val…
Non-invasive in vivo sensing of the lung with light would help diagnose and monitor pulmonary disorders (caused by e.g. COVID-19, emphysema, immature lung tissue in infants). We investigated the possibility to probe the lung with time domain diffuse optics, taking advantage of the increased depth (few cm) reached by photons detected after a long (few ns) propagation time. An initial study on 5 healthy volunteers included time-resolved broadband diffuse optical spectroscopy measurements at 3 cm source-detector distance over the 600-1100 nm range, and long-distance (6-9 cm) measurements at 820 n…
Norwegian University of Science and Technology（挪威科技大学）；SINTEF（挪威科技工业研究院）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain. Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework con…
Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow（莫斯科卫生保健部诊断与远程医疗技术研究与实用临床中心）
This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) Licens…
University of Iowa（爱荷华大学）；Boston University（波士顿大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Milano-Bicocca（米兰比可卡大学）；San Gerardo Hospital（圣杰拉多医院）
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidati…
Carnegie Mellon University（卡内基梅隆大学）；Louisiana State University（路易斯安那州立大学）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomark…
Saratoga High School（萨拉托加高中）；Stanford University（斯坦福大学）
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity on chest imaging has focused on segmenting computed tomography (CT) images; however, given that CTs are performed much less frequently than chest X-rays for COVID-19 patients, automated lung lesion segmentation on chest X-rays could be clinically valuable. There currently exists a universal shortage of chest X-rays with ground truth COVID-19 lung le…
Shahid Sadoughi University of Medical Sciences（沙希德·萨杜吉医科大学）；Mashhad University of Medical Sciences（马什哈德医科大学）；Geneva University Hospital（日内瓦大学医院）
This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In the first step, two residual networks were independently trained to identify the lung tissue for normal and Covid patients in a supervised manner. These two models, referred to as DL-Covid and DL-Norm for Covid-19 and normal patients, respectively, generate the voxel-wise probability maps for lung tissue identification. To detect Covid lesions, the CT image of the Covid patient is processed by the DL-Covid and DL-No…
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia~score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score est…
A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
Radboud University Medical Center（拉德堡德大学医学中心）；University of Brescia（布雷西亚大学）；Ospedale Sacro Cuore（圣十字医院）；Provincial Hospital of Bolzano (SABES-ASDAA)（博尔扎诺省医院）；University and Hospital Trust of Verona（威尼斯大学与医院信托）；Karolinska University Hospital（卡罗林斯卡大学医院）；Biopticka Laboratory, Ltd（Biopticka实验室）
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Information Science and Technology, Linyi University（临沂大学信息科学与技术学院）；Thoracic Surgery Department of Linyi Central Hospital（临沂市中心医院胸外科）；Personnel Department of Linyi Central Hospital（临沂市中心医院人事科）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a pixel level, we synthesize `les…
Nanjing University of Science and Technology（南京理工大学）；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）；China Electronics Cloud Brain (Tianjin) Technology CO., LTD（中电云脑（天津）科技有限公司）；Jiangsu University of Technology（江苏理工学院）；Fudan University（复旦大学）；University of Toronto（多伦多大学）；Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）；Lenovo Ltd.（联想集团）；Nanjing University（南京大学）
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep …
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Kyoto University Graduate School of Medicine（京都大学医学研究科）
Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. Materials and Methods: For lung segmentation, CXR images from the Japanese Society of Radiological Technology (JSRT, N = 247) and Montgomery databases (N = 138), were included, and 65 additional images depict…
Jordan University of Science and Technology（约旦科技大学）
Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detec…
Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）；Georgia Institute of Technology（佐治亚理工学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Cl…
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；ScreenPoint Medical（ScreenPoint Medical 公司）；Pingan Technology（平安科技）；Lunit Inc.（Lunit 公司）；Queen Mary University of London（伦敦玛丽女王大学）；Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submi…
University of Groningen（格罗宁根大学）；University Medical Center Groningen（格罗宁根大学医学中心）；University of Twente（特文特大学）
Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense…
Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase due to the domain shift. In this paper, we propose a novel unsupervised m…
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
University of Minnesota（明尼苏达大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medicine（威尔康奈尔医学院）；Mayo Clinic（梅奥诊所）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Yale University（耶鲁大学）；University of Pennsylvania（宾夕法尼亚大学）；Arizona State University（亚利桑那州立大学）；The University of Tokyo Hospital（东京大学医院）；Carnegie Mellon University（卡内基梅隆大学）；Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)（千禧年智能医疗工程研究所）；National Center for Artificial Intelligence (CENIA)（国家人工智能中心）；Xiamen University（厦门大学）；Seoul National University（首尔大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究院）；Thomas Jefferson University Hospital（托马斯杰斐逊大学医院）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；National Institutes of Health（美国国立卫生研究院）；National Library of Medicine（美国国家医学图书馆）
本文介绍 MICCAI 挑战赛 CXR-LT 2024，扩展大规模长尾胸部 X 光数据集并设置三项分类任务，汇总多模态、生成式与零样本方法以提升临床诊断泛化能力。
Monash University Malaysia（马来西亚莫纳什大学）；University of Malaya（马来亚大学）；University of Oklahoma（俄克拉荷马大学）
Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, they still depend on a convenient local filter such as convolution as full 3D attention is expensive to compute and requires a big dataset, which might not be available. Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i.e., self-attention). Unlike a regular Non-Local network, the 3D Axial-Attention …
Concordia Institute for Information Systems Engineering, Concordia University（康考迪亚大学信息系统工程研究所）；Sunnybrook Health Sciences Centre, University of Toronto（多伦多大学桑尼布鲁克健康科学中心）；McGill University Health Center- Research Institute（麦吉尔大学健康中心研究所）；Babak Imaging Center（巴布克影像中心）；Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）
Lung diseases including infections such as Pneumonia, Tuberculosis, and novel Coronavirus (COVID-19), together with Lung Cancer are significantly widespread and are, typically, considered life threatening. In particular, lung cancer is among the most common and deadliest cancers with a low 5-year survival rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save countless lives. In this regard, deep learning radiomics solutions have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated b…
Beihang University（北京航空航天大学）；Taiyuan University of Technology（太原理工大学）；Hefei Innovation Research Institute, Beihang University（北京航空航天大学合肥创新研究院）；Beijing Advanced Innovation Centre for Biomedical Engineering（北京生物医学工程高精尖创新中心）；Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine（北京大数据精准医疗高精尖创新中心）
The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relati…
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset compris…
Scuola Normale Superiore（比萨高等师范学校）；National Institute of Nuclear Physics (INFN)（国家核物理研究所）；University of Pavia（帕维亚大学）；University of Pisa（比萨大学）；University of Sassari（萨萨里大学）；University of Florence（佛罗伦萨大学）
The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung…
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Singapore General Hospital（新加坡中央医院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to chest X-ray data augmentation for lung disease recognition. However, the implausible and distorted pathology features generated from the less than perfect generator may lead to wrong clinical decisions. Why not keep the original pathology region? We proposed a novel approach that allows our generative model to generate high quality plausible images that contain undistorted pathology areas. The main idea is to design a training scheme based on an image-to-image translation network to introduce variation…
University College London（伦敦大学学院）；Frimley Health NHS Foundation Trust（弗林利健康NHS基金会信托）；Royal Berkshire NHS Foundation Trust（皇家伯克希尔NHS基金会信托）；Hospital de La Santa Creu I Sant Pau（圣十字圣保罗医院）；West Suffolk NHS Foundation Trust（西萨福克NHS基金会信托）；University of Cambridge（剑桥大学）；Great Ormond Street Children's Hospital NHS Foundation Trust（大奥蒙德街儿童医院NHS基金会信托）；University of Pavia（帕维亚大学）
Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural …
University of Electronic Science and Technology of China（电子科技大学）；University of Maryland School of Medicine（马里兰大学医学院）；Rutgers, the State University of New Jersey（罗格斯大学（新泽西州立大学））
Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-…
Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；McGill University（麦吉尔大学）
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) scans can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensiv…
Silesian University of Technology（西里西亚理工大学）；Medical University of Silesia（西里西亚医科大学）；Voivodship Specialist Hospital（省专科医院）；Central Clinical Hospital of the Ministry of Internal Affairs and Administration（内务与行政部中央临床医院）；Jagiellonian University Medical College（雅盖隆大学医学院）；Kujawsko-Pomorskie Pulmonology Center（库亚维-波美拉尼亚肺病中心）；Silesian Hospital（西里西亚医院）；Medical University of Warsaw（华沙医科大学）；Nicolaus Copernicus University（尼古拉斯·哥白尼大学）；Medical University of Bialystok（比亚韦斯托克医科大学）；Wroclaw Medical University（弗罗茨瓦夫医科大学）；Czerniakowski Hospital（切尔尼亚科夫斯基医院）；MEGREZ Hospital（MEGREZ医院）；Medical University of Gdansk（格但斯克医科大学）；Yale School of Medicine（耶鲁大学医学院）；The Maria Sklodowska-Curie National Research Institute of Oncology（玛丽·斯克沃多夫斯卡-居里国家肿瘤研究所）
The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients w…
Automated segmentation of pathological regions of interest aids medical image diagnostics and follow-up care. However, accurate pathological segmentations require high quality of annotated data that can be both cost and time intensive to generate. In this work, we propose an automated two-step method that detects a minimal image subset required to train segmentation models by evaluating the quality of medical images from 3D image stacks using a U-net++ model. These images that represent a lack of quality training can then be annotated and used to fully train a U-net-based segmentation model. T…
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
School of Electronic Engineering & Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；National Technical University Athens（雅典国立技术大学）
Medical Imaging Center, Vingroup Big Data Institute（Vingroup大数据研究院医学影像中心）；College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance …
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；University of Parma（帕尔马大学）；Shanghai MicroPort MedBot (Group) Co., Ltd.（上海微创医疗机器人（集团）股份有限公司）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Cambridge International Exam Centre in Shanghai Experimental School（上海实验学校剑桥国际考试中心）；InferVision Medical Technology Co., Ltd.（推想医疗科技股份有限公司）；Shanghai MediWorks Precision Instruments Co., Ltd（上海美沃克斯精密仪器有限公司）；Sanmed Biotech Ltd.（三曼生物科技有限公司）；University of Cambridge（剑桥大学）；University of Palermo（巴勒莫大学）；IRCCS Istituto delle Scienze Neurologiche di Bologna（IRCCS博洛尼亚神经科学研究所）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；INSA Rouen Normandie（鲁昂国立应用科学学院）；Univ Rouen Normandie（鲁昂诺曼底大学）；Université Le Havre Normandie（勒阿弗尔诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）；Wake Forest University School of Medicine（维克森林大学医学院）；University College London（伦敦大学学院）；King's College London（伦敦国王学院）
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…
Pulmonary lobe segmentation is an important preprocessing task for the analysis of lung diseases. Traditional methods relying on fissure detection or other anatomical features, such as the distribution of pulmonary vessels and airways, could provide reasonably accurate lobe segmentations. Deep learning based methods can outperform these traditional approaches, but require large datasets. Deep multi-task learning is expected to utilize labels of multiple different structures. However, commonly such labels are distributed over multiple datasets. In this paper, we proposed a multi-task semi-super…
Deep neural networks (DNNs) have been widely used for medical image analysis. However, the lack of access a to large-scale annotated dataset poses a great challenge, especially in the case of rare diseases, or new domains for the research society. Transfer of pre-trained features, from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumor, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbita…
Tianjin University（天津大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；La Trobe University（乐卓博大学）；Tianjin University of Traditional Chinese Medicine（天津中医药大学）
The insufficiency of annotated medical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new richer generative adversarial network for free-form 3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function. The RicherDG has dilated-gated convolution layers to enable tumor-painting and to enlarge perceptive fields; and it has a novel richer convolutional feature association bra…
The University of Hong Kong（香港大学）；Xidian University（西安电子科技大学）；Sun Yat-Sen University（中山大学）；DeepWise AI Lab（深睿医疗人工智能实验室）
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exp…
Beijing Jingzhen Medical Technology Ltd.（北京精诊医疗科技有限公司）；Tsinghua University（清华大学）；Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Xidian University（西安电子科技大学）
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing med…
ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；SSM Health, St. Louis, MO（SSM健康系统，圣路易斯，密苏里州）；Saint Louis University School of Medicine（圣路易斯大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山医院伊坎医学院）；National Strategic Technology Research Institute, Seoul National University Hospital（首尔国立大学医院国家战略技术研究机构）；Brigham and Women’s Hospital（布里金和妇女医院）；Chest Radiology Division, Medical Imaging Department, King Abdullah Specialized Children’s Hospital（国王阿卜杜勒阿齐兹特殊儿童医院放射科，医学成像部门）；King Abdulaziz Medical City, Ministry of National Guard Health Affairs（国王阿卜杜勒阿齐兹医疗城，国民卫队卫生事务部）
CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis
Silesian University of Technology（西里西亚工业大学）；Medical University of Silesia（西里西亚医科大学）；Voivodship Specialist Hospital（省立专科医院）；Central Clinical Hospital of the Ministry of Internal Affairs and Administration（内政与行政部中央临床医院）；Jagiellonian University Medical College（雅盖隆大学医学院）
