Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symmetric comparison of brain hemispheres is usually used to locate pathological abnormalities. Recent research has explored asymmetries to assist with AIS segmentation. However, most previous symmetry-based work mixed different types of asymmetries when evaluating their contribution to AIS. In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation. ADN first performs asymmetry disentanglement based on input NCCTs, which produces different types of 3D asymmetry maps. Then a synthetic, intrinsic-asymmetry-compensated and pathology-asymmetry-salient NCCT volume is generated and later used as input to a segmentation network. The training of ADN incorporates domain knowledge and adopts a tissue-type aware regularization loss function to encourage clinically-meaningful pathological asymmetry extraction. Coupled with an unsupervised 3D transformation network, ADN achieves state-of-the-art AIS segmentation performance on a public NCCT dataset. In addition to the superior performance, we believe the learned clinically-interpretable asymmetry maps can also provide insights towards a better understanding of AIS assessment. Our code is available at https://github.com/nihaomiao/MICCAI22_ADN.
This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state, i.e., acute ischemic stroke, and show how medical imaging and its analysis can be used to define the treatment. We show how the core-penumbra concept is used in practice using mismatch criteria and how machine learning can be used to make predictions of the final infarct, either via deconvolution or convolutional neural networks.
Molinette Hospital（莫利内特医院）；University of Turin（都灵大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Purpose: In this study we investigate whether a Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data in a clinical setting of patients with acute ischemic stroke. Methods: Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation. All the data used for the training/validation of the network and to generate ground truth (GT) maps, using a state-of-the-art deconvolution-algorithm, were previously pre-processed using a standard pipeline. Validation was carried out through manual segmentation of in…
Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks
Novosibirsk State University（新西伯利亚国立大学）；International Tomography Center SB RAS（西伯利亚分院国际断层扫描中心）；Sobolev Institute of Mathematics SB RAS（西伯利亚分院索博列夫数学研究所）
Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast Computed Tomography Images for Enhanced Treatment and Prognosis
Kwame Nkrumah University of Science and Technology（库马西科技大学）；Department of Computer Engineering（计算机工程系）；Kumasi Centre for Collaborative Research in Tropical Medicine（库马西热带医学协作研究中心）；Komfo Anokye Teaching Hospital（科莫福·阿诺耶教学医院）
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）
More than 13 million people suffer from ischemic cerebral stroke worldwide each year. Thrombolytic treatment can reduce brain damage but has a narrow treatment window. Computed Tomography Perfusion imaging is a commonly used primary assessment tool for stroke patients, and typically the radiologists will evaluate resulting parametric maps to estimate the affected areas, dead tissue (core), and the surrounding tissue at risk (penumbra), to decide further treatments. Different work has been reported, suggesting thresholds, and semi-automated methods, and in later years deep neural networks, for …
University of California, Los Angeles（加州大学洛杉矶分校）；VA Phoenix Healthcare System（凤凰城退伍军人医疗系统）
Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involve…
Kobe University Hospital（神户大学医院）；Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）；Ichinomiyanishi Nishi Hospital（一宮西医院）；Osaka Red Cross Hospital（大阪红十字医院）
Background and Purpose: We aimed to develop and evaluate an automatic acute ischemic stroke-related (AIS) detection system involving a two-stage deep learning model. Methods: We included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24 h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning de…
Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院模式识别与智能系统实验室）；Deepwise AI Lab（深度科技人工智能实验室）；Jinling Hospital, Nanjing University School of Medicine（南京大学医学院附属金陵医院）；Department of Computer Science and Technology, Peking University（北京大学计算机科学技术系）；The University of Hong Kong（香港大学）
Quantitative estimation of the acute ischemic infarct is crucial to improve neurological outcomes of the patients with stroke symptoms. Since the density of lesions is subtle and can be confounded by normal physiologic changes, anatomical asymmetry provides useful information to differentiate the ischemic and healthy brain tissue. In this paper, we propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The transformed im…
Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University（麻醉学、围术期与疼痛医学系，斯坦福大学）；Department of Biomedical Sciences, Seoul National University（生物医学科学系，首尔国立大学）；Department of Computer Engineering, National University of Sciences and Technology (NUST)（计算机工程系，国立科学与技术大学（NUST））；Department of Computer Science, The University of Alabama（计算机科学系，阿拉巴马大学）；Department of Computer Science, Hanyang University（计算机科学系，翰阳大学）
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning
Queens College of the City University of NY（纽约市立大学皇后学院）；National Chung Cheng University（台湾中正大学）；Ditmanson Medical Foundation Chia-Yi Christian Hospital（戴德森医疗财团法人嘉义基督教医院）
The University of Hong Kong（香港大学）；Hospital Authority（医院管理局）
The hyperdense middle cerebral artery (MCA) dot sign has been reported as an important factor in the diagnosis of acute ischemic stroke due to large vessel occlusion. Interpreting the initial CT brain scan in these patients requires high level of expertise, and has high inter-observer variability. An automated computerized interpretation of the urgent CT brain image, with an emphasis to pick up early signs of ischemic stroke will facilitate early patient diagnosis, triage, and shorten the door-to-revascularization time for these group of patients. In this paper, we present an automated detecti…
Diffusion-Weighted Magnetic Resonance Imaging (DWI) is widely used for early cerebral infarct detection caused by ischemic stroke. Manual segmentation is done by a radiologist as a common clinical process, nonetheless, challenges of cerebral infarct segmentation come from low resolution and uncertain boundaries. Many segmentation techniques have been proposed and proved by manual segmentation as gold standard. In order to reduce human error in research operation and clinical process, we adopt a semi-automatic segmentation as gold standard using Fluid-Attenuated Inversion-Recovery (FLAIR) Magne…
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. The p…
EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI
Yozgat Bozok University（亚兹加特博兹克大学）；Izmir Bakircay University（伊兹米尔巴克伊大学）；Queen Mary’s Digital Environment Research Institute (DERI)（皇后玛丽数字环境研究 institute (DERI)）；Firat University（费拉特大学）；Qatar University（卡塔尔大学）
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右岸医院）；School of Medicine, Technical University of Munich（慕尼黑工业大学医学院）；icometrix（icometrix公司）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；University of Bern（伯尔尼大学）；ARTORG Center for Biomedical Engineering Research, Univ. of Bern（伯尔尼大学生物医学工程研究ARTORG中心）；University of Southern California（南加州大学）；Chan Division of Occupational Science and Occupational Therapy, University of Southern California（南加州大学陈职业科学与职业治疗系）；TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中心）
Anatomy-Preserving Latent Diffusion for Generation of Brain Segmentation Masks with Ischemic Infarct
Department of Diagnostic Imaging Hospital de la Santa Creu i Sant Pau Barcelona, Spain（诊断影像部门 塞拉·克鲁与圣帕乌医院 巴塞罗那西班牙）；Department of Medical Physics Hospital de la Santa Creu i Sant Pau Barcelona, Spain（医学物理部门 塞拉·克鲁与圣帕乌医院 巴塞罗那西班牙）
Erasmus MC, University Medical Center Rotterdam（鹿特丹伊拉斯谟大学医学中心）；Leiden UMC（莱顿大学医学中心）；Maastricht UMC（马斯特里赫特大学医学中心）；Haaglanden Medical Center（海牙哈格兰登医学中心）
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a m…
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；University of St Andrews（圣安德鲁斯大学）
Heart Failure is a major component of healthcare expenditure and a leading cause of mortality worldwide. Despite higher inter-rater variability, endomyocardial biopsy (EMB) is still regarded as the standard technique, used to identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronary artery disease, myocardial infarction etc.) of unexplained heart failure. In this paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic. For this, we propose and implement a new unified architecture comprising CNN (inception-V3 model) and bidirectional LSTM (BiLSTM) with self-at…
Nankai University（南开大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Tianjin Huanhu Hospital（天津市环湖医院）
Ischemic stroke is a common disease in the elderly population, which can cause long-term disability and even death. However, the time window for treatment of ischemic stroke in its acute stage is very short. To fast localize and quantitively evaluate the acute ischemic stroke (AIS) lesions, many deep-learning-based lesion segmentation methods have been proposed in the literature, where a deep convolutional neural network (CNN) was trained on hundreds of fully labeled subjects with accurate annotations of AIS lesions. Despite that high segmentation accuracy can be achieved, the accurate labels …
The University of Hong Kong（香港大学）；Hospital Authority（医院管理局）
Large vessel occlusion (LVO) plays an important role in the diagnosis of acute ischemic stroke. Identifying LVO of patients in the early stage on admission would significantly lower the probabilities of suffering from severe effects due to stroke or even save their lives. In this paper, we utilized both structural and imaging data from all recorded acute ischemic stroke patients in Hong Kong. Total 300 patients (200 training and 100 testing) are used in this study. We established three hierarchical models based on demographic data, clinical data and features obtained from computerized tomograp…
Christian Medical College Ludhiana（卢迪亚纳基督教医学院）；Pacific Neuroscience Institute（太平洋神经科学研究所）
A stroke occurs when an artery in the brain ruptures and bleeds or when the blood supply to the brain is cut off. Blood and oxygen cannot reach the brain's tissues due to the rupture or obstruction resulting in tissue death. The Middle cerebral artery (MCA) is the largest cerebral artery and the most commonly damaged vessel in stroke. The quick onset of a focused neurological deficit caused by interruption of blood flow in the territory supplied by the MCA is known as an MCA stroke. Alberta stroke programme early CT score (ASPECTS) is used to estimate the extent of early ischemic changes in pa…
CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed from CTP data. However, CTP data or the perfusion parameters, are ambiguous to locate the infarct core or tissue at risk (penumbra), which is normally confirmed by the follow-up Diffusion Weighted Imaging (DWI) or perfusion diffusion mismatch. In this paper, we propose a novel generativ…
ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.；Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.；ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland.；Department of Radiation Oncology, University Hospital Bern, University of Bern.；Support Center of Advanced Neuroimaging (SCAN), University Institute of Diagnostic；University Institute of Diagnostic；Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland.；Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.；University of Zurich, Zurich, Switzerland.；Department of Diagnostic；Interventional Neuroradiology, School of Medicine；Health, TUM Klinikum, Technical University of Munich, Germany.；TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Germany.；Therapy, School of Medicine；Health, Technical University of Munich, Munich, Germany.；Department of Biomedical Engineering；Physics, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands；Department of Radiology, University of Calgary, Calgary, Canada；Hotchkiss Brain Institute, University of Calgary, Calgary, Canada；Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada；Heuron Co., Ltd., Seoul, South Korea；School of Electrical Engineering, Korea University, Seoul, Korea；University of Alberta, Alberta, Canada；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom；Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom；University of Calgary, Canada；University of Washington, Washington, United States；CREATIS, Universite Claude Bernard Lyon 1, INSA Lyon, UMR CNRS 5220, Inserm U1294, Villeurbanne, France；Department of Neurology, University Hospital Zurich, Zurich, Switzerland
Philips Research North America（飞利浦北美研究院）；MIT（麻省理工学院）
Infarcted brain tissue resulting from acute stroke readily shows up as hyperintense regions within diffusion-weighted magnetic resonance imaging (DWI). It has also been proposed that computed tomography perfusion (CTP) could alternatively be used to triage stroke patients, given improvements in speed and availability, as well as reduced cost. However, CTP has a lower signal to noise ratio compared to MR. In this work, we investigate whether a conditional mapping can be learned by a generative adversarial network to map CTP inputs to generated MR DWI that more clearly delineates hyperintense re…
The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·金梅尔综合癌症中心）；The Johns Hopkins University（约翰斯·霍普金斯大学）
The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging (PWI) methods. Each of these parameters has distinct radiological-pathological meaning. For example, DWI interrogates the movement of water in the tissue and PWI gives an estimate of the blood flow, both are critical me…
National University of Singapore（新加坡国立大学）；NIT, Tiruchirappalli（蒂鲁吉拉伯利国家技术学院）
Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment and treatment planning. In this work, we propose a segmentation model with adversarial learning for ischemic lesion segmentation. We adopt U-Net with skip connection and dropout as segmentation baseline network and a fully connected network (FCN) as discriminator network. Discriminator network consists of 5 convolution layers followed by leaky-ReLU and an upsampling layer to rescale the output to the size of the inp…
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）
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction
1 Department of Computer Science, University of Geneva, Switzerland 2 Department of Electrical \& Computer Engineering, University of Florida, FL, USA 3 Service of Medical Informatics, University Hospital of Geneva, Switzerland 4 Department of Imaging；Medical Informatics, University of Geneva, Switzerland
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
As one of the leading causes of mortality and disability worldwide, Acute Ischemic Stroke (AIS) occurs when the blood supply to the brain is suddenly interrupted because of a blocked artery. Within seconds of AIS onset, the brain cells surrounding the blocked artery die, which leads to the progression of the lesion. The automated and precise prediction of the existing lesion plays a vital role in the AIS treatment planning and prevention of further injuries. The current standard AIS assessment method, which thresholds the 3D measurement maps extracted from Computed Tomography Perfusion (CTP) i…
USC Stevens Neuroimaging and Informatics Institute（南加州大学史蒂文斯神经影像与信息学研究所）；Zilkha Neurogenetic Institute, Keck School of Medicine of USC（南加州大学凯克医学院齐尔卡神经遗传学研究所）；Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院马蒂诺斯生物医学成像中心）；Yale University（耶鲁大学）；University of Iowa（爱荷华大学）；Augusta University（奥古斯塔大学）；University of Texas（德克萨斯大学）；Kennedy Krieger Institute, Johns Hopkins University（约翰斯·霍普金斯大学肯尼迪·克里格研究所）
While ischemic stroke is a leading cause of death worldwide, there has been little success translating putative cerebroprotectants from rodent preclinical trials to human patients. We investigated computational image-based assessment tools for practical improvement of the quality, scalability, and outlook for large scale preclinical screening for potential therapeutic interventions in rodent models. We developed, evaluated, and deployed a pipeline for image-based stroke outcome quantification for the Stroke Preclinical Assessment Network (SPAN), a multi-site, multi-arm, multi-stage study evalu…
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Fudan University（复旦大学）；Jing’an District Centre Hospital of Shanghai（上海市静安区中心医院）
Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation. Our framework consists of three components based on Convolutional Neural Networks (CNNs) a…
During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. In previous work we proposed VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedu…
Background and Purpose: Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of vascular morphology in healthy and diseased adults including changes during aging and the anatomical variations in the Circle of Willis (CoW). Methods: We used our automatic method to segment and extract novel geometric features of the cerebral vasculature from MRA scans of 175 healthy subjects, 45 AIS, and 50 AD patients after spatial registration. This is followed by quantification and statistical analysis of vascular alterations in acute isc…
icometrix（爱科麦特克斯）；Technical University of Munich（慕尼黑工业大学）；KU Leuven（鲁汶大学）
Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core areas respectively). In clinical settings, the singular value decomposition (SVD) deconvolution is one of the most accepted and used approaches for generating interpretable and physically meaningful maps. Though this method has been widely validated in experimental and clinical settings, it might produce suboptimal results because the chosen inputs to the model cannot guarantee optimal performance. For the most crit…
