Detection of the Arterial Input Function Using DSC-MRI Data
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科斯基基辅理工学院”）
Svitlana Alkhimova, Kateryna Sazonova
备注：
7 pages
发表信息：
Proceedings of the V International Scientific and Practical Conference "Modern and global methods of the development of scientific thought", October 25-28, 2022, Florence, Italy. - Florence : International Science Group, 2022. - P. 541-547
Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed, and their advantages and disadvantages are listed.
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatment response. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation a…
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water. ASL is increasingly used in clinical studies due to its noninvasiveness, repeatability and benefits in quantification. However, ASL suffers from an inherently low-signal-to-noise ratio (SNR) requiring repeated measurements of control/spin-labeled (C/L) pairs to achieve a reasonable image quality, which in return increases motion sensitivity. This leads to clinically prolonged scanning times increasing the risk of motion artifacts. Thus, there is an immense need of adv…
University of Bern（伯尔尼大学）；Department of Neurology, Inselspital, University of Bern（伯尔尼大学 Inselspital 神经科）；Department of Neurology, University of California Los Angeles (UCLA)（加州大学洛杉矶分校（UCLA）神经科）；Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern（伯尔尼大学 Inselspital 诊断与介入神经放射学研究所高级神经影像支持中心）
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The proposed architecture is trained end-to-end and solely relies on raw perfusion data for inference. We used a dataset of 151 acute ischemic stroke cases for evaluation. Our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy.
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Radiology and Imaging Sciences（放射学与影像科学）；National Institutes of Health Clinical Center（国家卫生研究院临床中心）；Walter Reed National Military Medical Center（沃尔特·里德国家军事医学中心）；Biostatistics and Clinical Epidemiology Services（生物统计与临床流行病学服务）
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Department of Radiology, Charité - Universitätsmedizin Berlin（柏林查理医院放射科）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Diagnostic Image Analysis Group, Radboud University Medical Center（拉德堡德大学医学中心诊断图像分析组）；School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital（医学与健康学院，莱比锡大学医院）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg（埃朗根大学医院放射科，埃朗根-纽伦堡弗里德里希-亚历山大大学）；Laboratory for Computer Aided Medical Procedures, Technical University of Munich（慕尼黑技术大学计算机辅助医疗程序实验室）；Chair for AI in Medicine and Healthcare, Klinikum rechts der Isar, Technical University Munich（医学与健康中的AI教授职位，莱比锡大学医院，慕尼黑技术大学）
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI
Institute for Applied Medical Informatics, Institute of Computational Neuroscience, and Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf（应用医学信息学研究所、计算神经科学研究所和生物医学人工智能中心（bAIome）、汉堡医学院中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf（诊断与介入放射学及核医学系、汉堡医学院中心）；Department of Neurology, University Medical Center Hamburg-Eppendorf（神经病学系、汉堡医学院中心）
MOIS-SAM2是一种基于示例的Segment Anything Model 2，用于全身体部MRI中神经纤维瘤多病灶交互分割，实现了高精度和可扩展性，支持临床应用。
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
University of Sheffield（谢菲尔德大学）；Sheffield Teaching Hospitals（谢菲尔德教学医院）；INSIGNEO, Institute for in Silico Medicine, University of Sheffield（谢菲尔德大学INSIGNEO计算机模拟医学研究所）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学机器智能中心）
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style sc…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans w…
Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
The Netherlands Cancer Institute – Antoni van Leeuwenhoek（荷兰癌症研究所 – 安东尼·范·列文虎克）；National Center for Tumor Diseases (NCT), NCT/UCC Dresden（国家肿瘤疾病中心（NCT），NCT/UCC 德累斯顿）；DKFZ（德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学医学院及卡尔·古斯塔夫·卡鲁斯大学医院）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学触觉互联网与人机协同中心）；University of Twente（特文特大学）
University of Oxford（牛津大学）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院国民保健服务基金会信托）；King’s College London（伦敦国王学院）
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints t…
Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Radiology, Radiation Oncology, and Medical Physics, University of Ottawa（放射科、放射肿瘤学与医学物理系，渥太华大学）；Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute（心内科，医学系，渥太华心脏研究所）
Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning
School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）；Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所生物工程与计算实验室）；Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Alma Mater Studiorum – Università di Bologna（博洛尼亚大学医学与外科科学系（DIMEC））；Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所诊断与介入放射学）；Department of Biomedical and Neuromotor Sciences, University of Bologna（博洛尼亚大学生物医学与神经运动科学系）；nd Orthopedics and Trauma Unit, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所第二骨科与创伤单元）；Independent Researcher（独立研究员）
Addressing data annotation scarcity in Brain Tumor Segmentation on 3D MRI scan Using a Semi-Supervised Teacher-Student Framework
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education（脑机智能技术重点实验室，教育部）；Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics（深圳研究院，南京航空航天大学）
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Technical University of Munich（慕尼黑工业大学）
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The condition…
Tel Aviv University（特拉维夫大学）；Aix Marseille Univ（艾克斯-马赛大学）；New York University（纽约大学）
Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies. The infiltration of adipose tissue into the diseased muscle region varies in its severity across, and within, patients. In order to efficiently quantify the infiltration of fat, accurate segmentation of muscle and fat is needed. An estimation of the amount of infiltrated fat is typically done visually by experts. Several algorithmic solutions have been proposed for automatic segmentation. While these methods may work well in mild cases, t…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-networ…
Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed. Eighty-One T2-weighted MRI scans from 28 patients with non-small cell lung cancers were analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning model. This model augmented training data arising from 6 expert-segmented …
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…
Eastern Hepatobiliary Surgery Hospital（东方肝胆外科医院）；Ping An Technology（平安科技）；Georgia Institute of Technology（佐治亚理工学院）；PAII Inc.（PAII公司）
Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patient was scanned with T1WI, T2WI, T1WI venous phase (T2WI-V), T1WI arterial phase (T1WI-A), and DWI MRI sequences. We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing an…
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and a marker-controlled Watershed transformation based method. All methods were applied on breast MRI slices following selection of regio…
Anatomy-Informed Deep Learning and Radiomics for Automated Neurofibroma Segmentation in Whole-Body MRI
Institute for Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；Center for Biomedical Artificial Intelligence (bAIome)（生物医学人工智能中心）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine（诊断与介入放射学及核医学部门）；Department of Neurology（神经内科部门）
Vanderbilt University（范德堡大学）；Henry Jackson Foundation（亨利·杰克逊基金会）；University of Calgary（卡尔加里大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach has been shown to effectively segment whole brain non-contrast T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would enable broader application of volumetric assessment in multi-site studies. Transfer learning (TL) is commonly used to update the neural network weights for local factors; yet, it is commonly recognized to risk d…
Stroke Locus Net: Occluded Vessel Localization from MRI Modalities
Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar（电气工程系，工程学院，卡塔尔大学，多哈，卡塔尔）；Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar（数学与统计学系，艺术与科学学院，卡塔尔大学，多哈，卡塔尔）
本研究提出端到端深度学习流水线 Stroke Locus Net，结合 nnUNet 分割、动脉图谱与 pGAN 生成，仅用 MRI 扫描实现闭塞血管定位，为更快速的中风诊断提供潜力。
Stanford University（斯坦福大学）；GE Healthcare（通用电气医疗集团）；University of California Riverside（加州大学河滨分校）
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is considered the gold-standard for the measurement of CBF in humans. PET imaging, however, is not widely available because of its prohibitive costs and use of short-lived radiopharmaceutical tracers that typically require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more readily accessible and does not involve ionizing radiation. This study presents a…
H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation
Universidade Estadual de Campinas（坎皮纳斯州立大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University College London（伦敦大学学院）；Brunel University London（布鲁内尔伦敦大学）；Massachusetts Institute of Technology（麻省理工学院）
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）
Friedrich-Alexander-University Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；Universitätsklinikum Erlangen（埃尔朗根大学医院）
Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step in evaluating tumor volume and in the quantification of tumor characteristics. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and high variance in their intensity distribution across patients. In this paper, we propose a novel marker-controlled watershed transformation-based approach, which uses the brightest p…
University of Maryland School of Medicine（马里兰大学医学院）
Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and absolute measurement of cerebral blood flow (CBF). Arterial transit time (ATT) is a related physiological parameter reflecting the duration for the labeled spins to reach the brain region of interest. Multiple post-labeling delay (PLDs) can provide robust measures of both CBF and ATT, allowing for optimization of regional CBF modeling based on ATT. The prolonged acquisition time can potentially reduce the quality and accuracy of the CBF and ATT estimation. We proposed a novel network to significantly reduce the number…
Effect of Intensity Standardization on Deep Learning for WML Segmentation in Multi-Centre FLAIR MRI
Toronto Metropolitan University（多伦多都会大学）；University of Toronto（多伦多大学）；St. Michael’s Hospital（圣迈克尔医院）；Institute of Biomedical Engineering, Science and Technology (iBEST)（生物医学工程科学与技术研究所（iBEST））
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Universidad Industrial de Santander（桑坦德工业大学）；University of Toulouse（图卢兹大学）；Université Paul Sabatier Toulouse 3（图卢兹第三大学（保罗·萨巴蒂埃大学））；Bicêtre University Hospital（比塞特尔大学医院）；Assistance publique des hôpitaux de Paris（巴黎公共援助医院）；Université Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳里昂第一大学））
The rapid increment of morbidity of brain stroke in the last few years have been a driving force towards fast and accurate segmentation of stroke lesions from brain MRI images. With the recent development of deep-learning, computer-aided and segmentation methods of ischemic stroke lesions have been useful for clinicians in early diagnosis and treatment planning. However, most of these methods suffer from inaccurate and unreliable segmentation results because of their inability to capture sufficient contextual features from the MRI volumes. To meet these requirements, 3D convolutional neural ne…
University of Marburg（马尔堡大学）；University Hospital of Marburg（马尔堡大学医院）
In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra …
Technical University of Munich（慕尼黑工业大学）；GE Global Research（通用电气全球研发中心）；Harvard Medical School（哈佛医学院）
Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique that allows one to measure tissue perfusion in an organ of interest through the injection of an intravascular paramagnetic contrast agent (CA). Due to a preference for high temporal and spatial resolution in many applications, this modality could significantly benefit from accelerated data acquisitions. In this paper, we specifically address the problem of reconstructing perfusion MR image series from a subset of k-space data. Our proposed approach is motivated by the observation that temporal variations (dynamics) in …
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
The University of Sydney（悉尼大学）；Brain and Mind Centre（大脑与心智中心）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；School of Mathematics and Statistics, The University of Sydney（悉尼大学数学与统计学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）；Australian Imaging Service（澳大利亚影像服务机构）
Triple-Phase Sequential Fusion Network for Hepatobiliary Phase Liver MRI Synthesis
Yu-Yue Pathology Research Center, Jinfeng Laboratory（于越病理研究中心，锦峰实验室）；Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Army Medical University（临床实验室医学部，陆军军医大学第一附属医院）；Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China（智能协同计算实验室，电子科技大学）
