Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training. The trained model generates contextual CT images based on input masks. We quantify the quality of the images by using a fully convolutional network (FCN) score and blurriness. We also train another classification network to select better synthetic images. These synthetic CT images are then augmented to our hemorrhagic lesion segmentation network. By applying this augmentation method on 2.5%, 10% and 25% of original data, segmentation improved by 12.8%, 6% and 1.6% respectively.
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies, University of Granada（格拉纳达大学信息与通信技术研究中心）；Northwestern University（西北大学）；Center for Computational Imaging and Signal Analytics in Medicine, Northwestern University（西北大学医学计算成像与信号分析中心）
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. In this work, we describe our solution to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling. The final submission is an ensemble of 18 models. Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg.
Queen Elizabeth Hospital（伊丽莎白女王医院）；The Chinese University of Hong Kong（香港中文大学）
Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and…
We propose a feature generator backbone composed of an ensemble of convolutional neuralnetworks (CNNs) to improve the recently emerging Vision Transformer (ViT) models. We tackled the RSNA intracranial hemorrhage classification problem, i.e., identifying various hemorrhage types from computed tomography (CT) slices. We show that by gradually stacking several feature maps extracted using multiple Xception CNNs, we can develop a feature-rich input for the ViT model. Our approach allowed the ViT model to pay attention to relevant features at multiple levels. Moreover, pretraining the n CNNs using…
Capital Medical University（首都医科大学）；China National Clinical Research Center for Neurological Diseases（国家神经系统疾病临床医学研究中心）；Tencent（腾讯）；Shandong Jining Medical College（山东济宁医学院）
Background: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology based on non-contrast CT (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting. Materials and Methods: The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians's performance. We …
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel…
Florida Atlantic University（佛罗里达大西洋大学）；University of Technology（理工大学）；Babylon Health Directorate（巴比伦卫生局）
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each sl…
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended t…
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学北京天坛医院）
St. Michael’s Hospital（圣迈克尔医院）；University of Toronto（多伦多大学）
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demo…
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Northwestern University（西北大学）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））
Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）
Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC（先进计算与电子科学组，坎塔布里亚物理研究所（IFCA），CSIC-UC）；AI Technology for Life, Department of Computing and Information Sciences, Utrecht University（人工智能技术与生命，计算与信息科学系，乌得勒支大学）；Department of Radiology, The Netherlands Cancer Institute（放射科，荷兰癌症研究所）；Servicio de Radiología, Hospital Reina Sofía（放射科，雷娜·索菲亚医院）；Servicio de Radiología, Hospital Universitario Rey Juan Carlos（放射科，雷昂·卡洛斯大学医院）；Departamento de Tecnologías de La Información, Universidad CEU San Pablo（信息技术部，CEU桑帕布洛大学）
Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.
We propose Artificial Intelligence Prospective Randomized Observer Blinding Evaluation (AI-PROBE) for quantitative clinical performance evaluation of radiology AI systems within prospective randomized clinical trials. AI-PROBE encompasses a study design and a matching radiology IT infrastructure that randomly blinds radiologists for results provided by AI-based image analysis. To demonstrate the applicability of our evaluation framework, we present a first prospective randomized clinical trial on the effect of Intra-Cranial Hemorrhage (ICH) detection in emergent care head CT on radiology study…
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Union Strong Technology Beijing Co.（北京联强科技公司）；The University of Virginia（弗吉尼亚大学）；Beijing Tiantan hospital（北京天坛医院）
Intracranial aneurysm (IA) is a life-threatening blood spot in human's brain if it ruptures and causes cerebral hemorrhage. It is challenging to detect whether an IA has ruptured from medical images. In this paper, we propose a novel graph based neural network named GraphNet to detect IA rupture from 3D surface data. GraphNet is based on graph convolution network (GCN) and is designed for graph-level classification and node-level segmentation. The network uses GCN blocks to extract surface local features and pools to global features. 1250 patient data including 385 ruptured and 865 unruptured …
University of Pennsylvania（宾夕法尼亚大学）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Bern（伯尔尼大学）；Weill Cornell Medical College（威尔康奈尔医学院）；Boston University（波士顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Washington（华盛顿大学）
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenien…
SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing
Visual Computing and Artificial Intelligence, Kiel University, Germany（视觉计算与人工智能研究所，基尔大学，德国）；Institute for Medical Informatics and Statistics, Kiel University, Germany（医学信息学与统计研究所，基尔大学，德国）；Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany（神经放射科，马格德堡大学医学学院，德国）；Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Germany（放射学与神经放射学部门，石勒苏益格-荷尔斯泰因大学医院，德国）；Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland（数学与计算机科学学院，亚当·密茨凯维奇大学，波兰）
Sberbank AI Lab（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.
deepc GmbH（deepc有限公司）；Technische Universität München（慕尼黑工业大学）
High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few traini…
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学影像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院放射科）
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-pos…
King’s College London（伦敦国王学院）；National Hospital for Neurology and Neurosurgery（国家神经内科和神经外科医院）；Thomas Jefferson University（托马斯·杰斐逊大学）；University of Washington（华盛顿大学）
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical.…
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany（数据与知识工程组，计算机科学系，奥托·冯·格里克大学马格德堡分校）；Genomics Research Centre, Human Technopole, Milan, Italy（基因组研究中心，人类技术pole，米兰，意大利）；Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, Germany（流体动力学与技术流动实验室，奥托·冯·格里克大学马格德堡分校）；Department of Biomedical Magnetic Resonance, Faculty of Natural Sciences, Otto von Guericke University Magdeburg, Germany（生物医学磁共振部门，自然科学系，奥托·冯·格里克大学马格德堡分校）；MedDigit, Department of Neurology, Medical Faculty, University Hospital, Magdeburg, Germany（MedDigit，神经病学部门，医学学院，大学医院，马格德堡，德国）；German Centre for Neurodegenerative Disease, Magdeburg, Germany（德国神经退行性疾病中心，马格德堡，德国）；Centre for Behavioural Brain Sciences, Magdeburg, Germany（行为脑科学中心，马格德堡，德国）；School of Mathematics & Statistics, UNSW Sydney and UNSW Data Science Hub（数学与统计学学院，悉尼大学和UNSW数据科学中心）
本文提出 PULASki，在 Probabilistic UNet 中引入基于统计距离的损失以建模专家标注差异，并在颅内血管和 MS 病灶任务上显著优于四种基线。
Research Institute for Signals, Systems and Computational Intelligence, sinc(i), CONICET, FICH-UNL（信号、系统与计算智能研究所 (sinc(i))，国家科学与技术研究理事会 (CONICET)，卢汉国立大学工程学院 (FICH-UNL)）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure. In these cases, CT scans are obtained to diagnose and assess injuries, or guide a certain therapy and intervention. We propose a deep learning based method to reconstruct the skull defect removed during DC performed after TBI from post-operative CT images. This reconstruction is useful in multiple scen…
A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images
Computer and Information Science College, King Saud University（沙特王后大学计算机与信息科学学院）；Department of Ophthalmology, College of Medicine, Princess Nourah bint Abdulrahman University（普里丝特努尔·阿卜杜勒拉赫曼大学医学院眼科系）
TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset
Tongji University（同济大学）；Tongji Hospital, School of Medicine, Tongji University（同济大学附属同济医院）；Tongji Eye Institute, School of Medicine, Tongji University（同济大学医学院同济眼科研究所）
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Duke University Medical Center（杜克大学医学中心）；Indiana University School of Medicine（印第安纳大学医学院）；Indiana University（印第安纳大学）；Thomas Jefferson University（托马斯杰斐逊大学）；University of California San Francisco（加利福尼亚大学旧金山分校）；Yale University（耶鲁大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Missouri（密苏里大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；University of Pennsylvania（宾夕法尼亚大学）；Helmholtz Munich（亥姆霍兹慕尼黑）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；University of California San Francisco (UCSF)（加利福尼亚大学旧金山分校）
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）；GlaxoSmithKline（葛兰素史克）
Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no interest in a particular individual's information but rather aim to derive insights at the level of cohorts. Here, we utilize Generative Adversarial Networks (GANs) to create derived medical imaging datasets consisting entirely of synthetic patient data. The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information. We ass…
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of Glasgow（格拉斯哥大学）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection perform…
Ben-Gurion University（本-古里安大学）；Soroka Medical Center（索罗卡医学中心）
The problem of inter-rater variability is often discussed in the context of manual labeling of medical images. The emergence of data-driven approaches such as Deep Neural Networks (DNNs) brought this issue of raters' disagreement to the front-stage. In this paper, we highlight the issue of inter-rater bias as opposed to random inter-observer variability and demonstrate its influence on DNN training, leading to different segmentation results for the same input images. In fact, lower overlap scores are obtained between the outputs of a DNN trained on annotations of one rater and tested on anothe…
