A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Technical University of Munich（慕尼黑工业大学）；University Medical Center Freiburg（弗莱堡大学医学中心）
Hans Liebl, David Schinz, Anjany Sekuboyina, Luca Malagutti, Maximilian T. Löffler, Amirhossein Bayat, Malek El Husseini, Giles Tetteh, Katharina Grau, Eva Nied… 展开作者
Hans Liebl, David Schinz, Anjany Sekuboyina, Luca Malagutti, Maximilian T. Löffler, Amirhossein Bayat, Malek El Husseini, Giles Tetteh, Katharina Grau, Eva Niederreiter, Thomas Baum, Benedikt Wiestler, Bjoern Menze, Rickmer Braren, Claus Zimmer, Jan S. Kirschke
备注：
18 pages, 2 figures, 2 tables; Hans Liebl, David Schinz equally contributed to this manuscript
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n=77) and transitional vertebrae (n=161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.
University of Kentucky（肯塔基大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to harmonize CT image data captured using different scanners is vital in cross-center large-scale radiomics studies but remains the boundary to explore. Furthermore, the lack of paired training image problem makes it computationally challenging to adopt existing deep learning models. %developed for CT image standardization. %this problem more challenging. We propose a novel deep learning approach called CVH-CT for harmonizing CT imag…
King's College London（伦敦国王学院）；Eindhoven University of Technology（埃因霍温理工大学）
Cine cardiac magnetic resonance (CMR) has become the gold standard for the non-invasive evaluation of cardiac function. In particular, it allows the accurate quantification of functional parameters including the chamber volumes and ejection fraction. Deep learning has shown the potential to automate the requisite cardiac structure segmentation. However, the lack of robustness of deep learning models has hindered their widespread clinical adoption. Due to differences in the data characteristics, neural networks trained on data from a specific scanner are not guaranteed to generalise well to dat…
Nanjing University of Science and Technology（南京理工大学）
Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac structure segmentation if training cases and testing cases are from the same distribution. However, the performance would be degraded if the testing cases are from a distinct domain (e.g., new MRI scanners, clinical centers). In this paper, we propose a histogram matching (HM) data augmentation method to eliminate the domain gap. Specifically, our method generates new training cases by using HM to transfer the intensity distribution of testing cases to existing training cases. The proposed method is quite simple and c…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Generalizability is a concern when applying a deep learning (DL) model trained on one dataset to other datasets. Training a universal model that works anywhere, anytime, for anybody is unrealistic. In this work, we demonstrate the generalizability problem, then explore potential solutions based on transfer learning (TL) by using the cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion task as the testbed. Previous works have converted CBCT to CT-like images. However, all of those works studied only one or two anatomical sites and used images from the same vendor's …
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to improve the reliability of deep learning classification models by proposing Uncertainty-Based Instance eXclusion (UBIX). This technique, based on multiple-instance learning, reduces the effect of corrupted instances on the b…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Medical Faculty Heidelberg, Heidelberg University（海德堡大学海德堡医学院）
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden of time-consuming manual contouring of cardiac structures. Moreover, frameworks such as nnU-Net provide entirely automatic model configuration to unseen datasets enabling out-of-the-box application even by non-experts. However, current studies commonly neglect the clinically realistic scenario, in which a trained network is applied to data from a different domain such as deviati…
National University of Sciences and Technology（国立科技大学）；Khalifa University（哈利法大学）
Pulmonary diseases can cause severe respiratory problems, leading to sudden death if not treated timely. Many researchers have utilized deep learning systems to diagnose pulmonary disorders using chest X-rays (CXRs). However, such systems require exhaustive training efforts on large-scale data to effectively diagnose chest abnormalities. Furthermore, procuring such large-scale data is often infeasible and impractical, especially for rare diseases. With the recent advances in incremental learning, researchers have periodically tuned deep neural networks to learn different classification tasks w…
Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm U1216（法国国家健康与医学研究院 U1216）；Grenoble Institut for Neurosciences（格勒诺布尔神经科学研究所）
ImUnity is an original deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D-slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its self-supervised training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition …
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
Radiologisches Institut, Universitätsklinikum Erlangen（埃朗根大学医院放射学研究所）；School of Biomedical Engineering and Imaging, KCL（KCL生物医学工程与成像学院）；Institute for Information Processing, Leibniz University Hannover（汉诺威莱布尼茨大学信息处理研究所）；CAIMed, L3S research institute（CAIMed，L3S研究机构）
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯NHS基金会信托）；Duke University（杜克大学）；Barts Health NHS Trust（巴特健康NHS基金会信托）；Alan Turing Institute（阿兰·图灵研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）
Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis
Indiana University School of Medicine（印第安纳大学医学院）；Purdue University（普渡大学）；University of Calgary（卡尔加里大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Duke University（杜克大学）；The Ohio State University（俄亥俄州立大学）；Weill Cornell Medical Center（威尔康奈尔医学中心）；OhioHealth（俄亥俄健康）
Shenzhen University（深圳大学）；Universitat de Barcelona（巴塞罗那大学）；University of Leeds（利兹大学）；KU Leuven（荷语鲁汶大学）
The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced in multi-centre studies involving data acquired using multi-vendor scanners, with variations in acquisition protocols. It is challenging to address this degradation because the shift is often not known \textit{a priori} and hence difficult to model. We propose a novel framework to ensure robust segmentation in the presence of such distribution shifts. Our contribution is three-fold. First, inspired by the spirit of c…
CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the reconstructed images, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifacts in DECT, we introduce a metal-artifact reduction scheme for iterative DECT algorithms. An estimate is substituted for the corrupt data in each iteration. We u…
University of Amsterdam（阿姆斯特丹大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable. In this work, we leverage information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquis…
Proscia Inc.（Proscia 公司）；Boonshoft School of Medicine, Wright State University School of Medicine（莱特州立大学医学院布恩肖夫特医学院）；Dermatopathology Laboratory of Central States（中部各州皮肤病理学实验室）；University of Florida College of Medicine（佛罗里达大学医学院）；Cockerell Dermatopathology（科克雷尔皮肤病理学机构）；Sidney Kimmel Medical College at Thomas Jefferson University（托马斯杰斐逊大学西德尼·金梅尔医学院）
Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin \& eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's …
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
INSA-Lyon, Universite Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂国立应用科学学院、里昂第一大学克洛德·贝尔纳大学、圣艾蒂安大学、法国国家科学研究中心、法国国家健康与医学研究院、CREATIS联合研究机构）；Institut Universitaire de France (IUF)（法兰西大学研究院）；University of Sherbrooke（谢布鲁克大学）；Huazhong University of Science and Technology（华中科技大学）；Sunnybrook Research Institute, University of Toronto（多伦多大学桑尼布鲁克研究所）；Imperial College London（伦敦帝国理工学院）；University College London（伦敦大学学院）；Fudan University（复旦大学）；Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨技术大学）；Universidad de Zaragoza（萨拉戈萨大学）；Instituto de Investigación Sanitaria de Aragón (IIS Aragón)（阿拉贡卫生研究所）；Centre Hospitalier Annecy Genevois（阿讷西日内瓦中心医院）；Hôpital Universitaire Cardiologique Louis Pradel（路易·普拉德尔大学心脏病医院）
NIMARC-MRI: Abdominal HASTE Dataset and a Baseline U-Net Exposing the Synthetic-to-Real Gap in Low-Resource Motion Correction
Federal University of Health Sciences, Azare（阿扎雷联邦健康科学大学）；African Institute for Research Advancement and Innovation (AIRA Africa)（非洲研究与创新促进研究所）；Bayero University Kano（卡诺拜鲁克大学）；Medserve Kano Diagnostic Center（卡诺医疗服务中心）；Radiology, University of Maiduguri Teaching Hospital（迈杜古里教学医院放射科）；PET Imaging Centre, King’s College London, University of London（伦敦大学国王学院正电子发射断层扫描成像中心）；Northwest University kano（卡诺西北大学）
