One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in noisy annotations and, consequently, inaccurate predictions.
To avoid that problem in the task of computed tomography (CT) imaging segmentation we propose a clearing algorithm for annotations. It consists of 3 stages:
- annotators scoring, which assigns a higher confidence level to better annotators;
- nodules scoring, which assigns a higher confidence level to nodules confirmed by good annotators;
- nodules merging, which aggregates annotations according to nodules confidence.
In general, the algorithm can be applied to many different tasks (namely, binary and multi-class semantic segmentation, and also with trivial adjustments to classification and regression) where there are several annotators labeling each image.
Novosibirsk State University（新西伯利亚国立大学）；International Tomography Center SB RAS（俄罗斯科学院西伯利亚分院国际断层成像中心）；Sobolev Institute of Mathematics SB RAS（俄罗斯科学院西伯利亚分院索博列夫数学研究所）
This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke on the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architec…
National Institutes of Health (NIH)（美国国立卫生研究院）；The NIAID Integrated Research Facility（NIAID综合研究设施）；University of Pennsylvania（宾夕法尼亚大学）
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and …
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；University of Zurich（苏黎世大学）
Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matc…
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Brigham and Women's Hospital（布莱根妇女医院）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；PixelMed Publishing（PixelMed出版公司）；Massachusetts General Hospital（麻省总医院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）
Qatar University（卡塔尔大学）；Institute of Research for Development（发展研究所）；French National Institute for Agricultural Research, INRA（法国国家农业研究院（INRA））；University of Burgundy（勃艮第大学）；Aarhus University（奥胡斯大学）；Tampere University of Technology（坦佩雷理工大学）；AgroParisTech（巴黎高科农业学院）
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation. Moreover, absence of a unified benchmark prohibits quantitative evaluation, which further clouds the impact of existing methodologies. In this study, we tackle the issue on both fronts. Firstly, by drawing parallels with natural image segmentation, w…
Eindhoven University of Technology（埃因霍温理工大学）；Erasmus MC - University Medical Center Rotterdam（伊拉斯谟医学中心-鹿特丹大学医学中心）；Erasmus MC - Sophia Children’s Hospital（伊拉斯谟医学中心-索菲亚儿童医院）；University of Copenhagen（哥本哈根大学）
Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts…
University of New South Wales（新南威尔士大学）；UNSW Sydney（新南威尔士大学悉尼分校）；Prince of Wales Hospital（威尔士亲王医院）；Auckland City Hospital（奥克兰市医院）
Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written cons…
Unsupervised Semantic Segmentation in Synchrotron Computed Tomography with Self-Correcting Pseudo Labels
Data Science and Learning Division, Argonne National Laboratory（Argonne国家实验室数据科学与学习部门）；X-Ray Science Division, Argonne National Laboratory（Argonne国家实验室X射线科学部门）
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Brigham and Women’s Hospital Harvard Medical School（哈佛医学院布莱尔妇女医院）；Khoury College of Computer Sciences Northeastern University（东北大学计算机科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；Dana-Farber Cancer Institute Harvard Medical School（哈佛医学院丹娜-法伯癌症研究院）
University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Bascom Palmer Eye Institute（巴斯科姆·帕尔默眼科研究所）
Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the retina. OCT images from 10 patients with mild non-proliferative diabetic retinopathy were used from a public (U. of Miami) dataset. For each patient, five images were available: one image of the fovea center, two images of the perifovea, and two images of the parafovea. For each image, two expert graders each manually annotated five retinal surfaces (i.e. boundaries between pairs of retinal layers). The first grader's a…
National Yang Ming Chiao Tung University（阳明交通大学）；National Cheng Kung University Hospital（国立成功大学医学院附设医院）；National Cheng Kung University（国立成功大学）
This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. However, the supervised learning models need to be retrained and the images need to be relabeled when the CTPA images come from different hospitals. This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of un…
Vidyasirimedhi Institute of Science & Technology（维迪亚西里梅迪科技学院）；Kamnoetvidya Science Academy（卡姆诺维迪亚科学学院）；Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院）；Cixi Institute of Biomedical Engineering（慈溪生物医学工程研究所）；Kasetsart University（泰国农业大学）；Nanyang Technological University（南洋理工大学）
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem. In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation. The proposed method, called OCTAve, combines the weakly-supervised learning using scribble-annotated ground truth …
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Helmholtz Zentrum Muenchen（亥姆霍兹慕尼黑中心）；University of Innsbruck（因斯布鲁克大学）；University of Oxford（牛津大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Boehringer Ingelheim Limited（勃林格殷格翰有限公司）；University College London（伦敦大学学院）；Cairo University（开罗大学）；Watany Eye Hospital（瓦塔尼眼科医院）；Heidelberg Engineering GmbH（海德堡工程有限公司）；University of Zurich（苏黎世大学）
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）
Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addit…
Imperial College London（伦敦帝国学院）；Aichi Cancer Center（爱知县癌症中心）；Nagoya University（名古屋大学）
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort. However, when this concept is ported to the medical imaging domain, reading expertise will have a direct impact on the annotation accuracy. In this study, we examine the impact of expertise and the amount of available annotations on the accuracy outcome of a liver segmentation problem in an abdominal computed tomography (CT) image database. In controlled experiments, we study this impact for different types of w…
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This manuscript details our contribution to MICCAI's 2022 Airway Tree Modelling challenge, a competition of fully automated methods for airway segmentation. We employed a previously developed deep learning architecture based on a modified EfficientDet (MEDSeg), training from scratch for binary airway segmentation using the provided annotations. Our method achieved 90.72 Dice in internal validation, 95.52 Dice on external va…
INESC TEC（INESC TEC科技与创新研究所）；Faculdade de Ciências da Universidade do Porto（波尔图大学理学院）；Champalimaud Clinical Center（尚帕利莫临床中心）；Hospital Pedro Hispano（佩德罗·伊斯帕诺医院）；Nova Medical School（诺瓦医学院）
The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels i…
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, 39762, USA（工业与系统工程系，密苏里州立大学）；Department of Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, 39216, USA（放射肿瘤学系，密苏里大学医学中心）
Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art methods usually treat BMA removal as a learning-based image inpainting problem, but require numerous training samples with nontrivial annotation. In addition, these methods discard the rich structural and appearance information carried in the BMA stripe region. To address thes…
South China University of Technology（华南理工大学）；Fudan University（复旦大学）；University of Notre Dame（圣母大学）；Guangdong Cardiovascular Institute（广东省心血管病研究所）
Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence,and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiograp…
Nanjing University of Science and Technology（南京理工大学）
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / d…
Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究所（INESC TEC））；Faculty of Engineering of the University of Porto (FEUP)（波尔图大学工程学院（FEUP））；University of Trás-os-Montes e Alto Douro (UTAD)（山后-上杜罗大学（UTAD））；Centro Hospitalar e Universitário de S. João（圣若昂大学医院中心）；Faculty of Medicine of the University of Porto (FMUP)（波尔图大学医学院（FMUP））
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly, time-consuming and prone to variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to clinical routine is challenging. In this study, a new database for the development and testing of pulmonary nodule computer-aided strategies is presented which intends…
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）
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providing annotations. Automatic and semi-automatic tumor segmentation methods have recently shown promising results. However, as different researchers have validated their algorithms using various datasets and performance metrics, reliably evaluating these …
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Department of Radiology, Dokuz Eylul University（多尔朱·伊勒大学放射科）；Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Institute of Natural and Applied Sciences, Dokuz Eylul University（多尔朱·伊勒大学自然科学与应用科学学院）；Department of Electrical and Electronics Engineering, Dokuz Eylul University（多尔朱·伊勒大学电气与电子工程系）；Department of Radiology, University of Wisconsin-Madison（威斯康星大学麦迪逊分校放射科）；School of Medicine, Sivas Cumhuriyet University（萨瓦斯·库尔德大学医学院）；School of Medicine, Acibadem Mehmet Ali Aydinlar University（阿克塞姆·梅赫梅特·阿里·阿迪姆大学医学院）；Department of Artificial Intelligence Engineering, Bahçeşehir University（巴切希尔大学人工智能工程系）；Faculty of Pharmacy, Sivas Cumhuriyet University（萨瓦斯·库尔德大学药学院）
3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
New York University, Center for Data Science（纽约大学数据科学中心）；New York University, Courant Institute of Mathematical Sciences（纽约大学Courant数学科学研究所）；NYU Grossman School of Medicine, Department of Radiology（纽约大学 Grossman 医学院放射科）；Siemens Healthineers（西门子医疗科技）；NYU Grossman School of Medicine, Department of Neurology（纽约大学 Grossman 医学院神经病学部）；NYU Grossman School of Medicine, Department of Neuroscience and Physiology（纽约大学 Grossman 医学院神经科学与生理学部）；NYU Grossman School of Medicine, Neuroscience Institute（纽约大学 Grossman 医学院神经科学研究所）
Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院）；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所）
We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices…
Queen Mary University of London（伦敦玛丽女王大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）；University of Electronic Science and Technology of China（电子科技大学）；Hangzhou Dianzi University（杭州电子科技大学）
3D tooth segmentation is a prerequisite for computer-aided dental diagnosis and treatment. However, segmenting all tooth regions manually is subjective and time-consuming. Recently, deep learning-based segmentation methods produce convincing results and reduce manual annotation efforts, but it requires a large quantity of ground truth for training. To our knowledge, there are few tooth data available for the 3D segmentation study. In this paper, we establish a fully annotated cone beam computed tomography dataset CTooth with tooth gold standard. This dataset contains 22 volumes (7363 slices) w…
University of Pittsburgh School of Medicine（匹兹堡大学医学院）；VA Pittsburgh Healthcare System（匹兹堡退伍军人医疗系统）
Purpose: To develop and validate a computer tool for automatic and simultaneous segmentation of body composition depicted on computed tomography (CT) scans for the following tissues: visceral adipose (VAT), subcutaneous adipose (SAT), intermuscular adipose (IMAT), skeletal muscle (SM), and bone. Approach: A cohort of 100 CT scans acquired from The Cancer Imaging Archive (TCIA) was used - 50 whole-body positron emission tomography (PET)-CTs, 25 chest, and 25 abdominal. Five different body compositions were manually annotated (VAT, SAT, IMAT, SM, and bone). A training-while-annotating strategy w…
Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院布里格姆医疗系统）；Harvard-MIT Division of Health Sciences & Technology（哈佛-麻省理工学院健康科学与技术部）；Maastricht University（马斯特里赫特大学）
Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) …
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西达赛奈医学中心生物医学成像研究所）；Cedars-Sinai Medical Center（西达赛奈医学中心）；Semmelweis University（塞梅尔维斯大学）；Azienda Ospedaliero-Universitaria di Parma（帕尔马大学医院）；Monash Health（莫纳什医疗中心）；Centro Cardiologico Monzino IRCCS, University of Milan（米兰大学蒙齐诺心脏中心IRCCS）；Showa University School of Medicine（昭和大学医学院）
Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for rapid quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performe…
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）
The Second Hospital of Shandong University, Shandong University（山东大学第二医院，山东大学）；Shenzhen Third People’s Hospital, the Second Affiliated Hospital of Southern University of Science and Technology（深圳市第三人民医院，南方科技大学第二附属医院）；The First Affiliated Hospital of Hainan Medical University（海南医科大学第一附属医院）；Wuhan Asia General Hospital（武汉亚洲总医院）；Shanxi Medical University（山西医科大学）；Chengdu First People’s Hospital（成都市第一人民医院）；Keck School of Medicine, University of Southern California（南加州大学凯克医学院）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）
Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could au…
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Zhejiang Sci-Tech University（浙江理工大学）；Lishui University（丽水学院）；the Second Affiliated Hospital of Zhejiang University, College of Medicine（浙江大学医学院附属第二医院）；Communication University of Zhejiang（浙江传媒学院）；Hangzhou Dianzi University（杭州电子科技大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
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）
Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomography
Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China（复旦大学人工智能创新与孵化院，上海，中国）；Shanghai Academy of Artificial Intelligence for Science, Shanghai, China（上海人工智能科学研究院，上海，中国）；Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, China（复旦大学华山医院核医学科与PET中心）；Human Phenome Institute, Fudan University, Shanghai, China（复旦大学人类表型研究院）；Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China（浙江省肿瘤医院核医学科，杭州医学院，中国科学院，杭州，中国）；Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China（上海通用医学影像诊断中心，上海，中国）；Hangzhou Universal Medical Imaging Diagnostic Center, Hangzhou, China（杭州通用医学影像诊断中心，杭州，中国）；Department of Information and Intelligence Development, Zhongshan Hospital, Fudan University, Shanghai, China（复旦大学中山医院信息与智能发展科）；Department of Nuclear Medicine, Inselspital, University of Bern, Switzerland（伯尔尼大学Inselspital核医学科，瑞士）
UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases
Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology（北京工业大学计算智能与智能系统北京市重点实验室）；Yanjing Medical College, Capital Medical University（首都医科大学燕京医学院）
本文介绍多中心UltraEar数据库的构建，整合11家医院0.1 mm U-HRCT影像、报告与临床数据，并提供标准化标注、分割和隐私保护流程，以支持耳科AI研究、教学与协作。
All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing
Callisto Inc（卡利斯托公司）；The University of Tokyo（东京大学）；St. Marianna University School of Medicine（圣玛丽安娜医科大学医学院）；Harada Academy（原田学园）；St. Marianna University Yokohama Seibu Hospital（圣玛丽安娜大学横滨西部医院）
Segmentation of objects of interest is one of the central tasks in medical image analysis, which is indispensable for quantitative analysis. When developing machine-learning based methods for automated segmentation, manual annotations are usually used as the ground truth toward which the models learn to mimic. While the bulky parts of the segmentation targets are relatively easy to label, the peripheral areas are often difficult to handle due to ambiguous boundaries and the partial volume effect, etc., and are likely to be labeled with uncertainty. This uncertainty in labeling may, in turn, re…
A practical artificial intelligence framework for legal age estimation using clavicle computed tomography scans
Panacea Cooperative Research S.Coop.；Department of Computer Science and Artificial Intelligence, University of Granada（计算机科学与人工智能系，格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute)（数据科学与计算智能安达卢西亚研究所（DaSCI研究所））；Department of Organisms and Systems Biology, Faculty of Biology, University of Oviedo（生物系，奥维耶多大学 organisms and Systems Biology 系）；Faculty of Computer Science, CITIC, University of A Coruña（计算机科学系，阿拉斯加州立大学）
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
City University of Hong Kong（香港城市大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of California, San Francisco（加州大学旧金山分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Chinese University of Hong Kong（香港中文大学）；NVIDIA（英伟达）
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Xiamen University（厦门大学）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Tencent YouTu Lab（腾讯优图实验室）；Southern University of Science and Technology（南方科技大学）
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南芳PET中心，南方医科大学南芳医院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University（广东省医学图像处理重点实验室，南方医科大学）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University（广东省医学影像与诊断技术工程实验室，南方医科大学）；Pazhou Lab（琶洲实验室）
