Syed M. Arshad, Lee C. Potter, Chong Chen, Yingmin Liu, Preethi Chandrasekaran, Christopher Crabtree, Matthew S. Tong, Orlando P. Simonetti, Yuchi Han, Rizwan Ahmad
发表信息：
Magnetic Resonance in Medicine 92(3) (2024) 1248-1262
目的：提出并评估一种异常值抑制方法，使自由运行式容积心血管MRI（CMR）对运动更具鲁棒性。方法：所提出的方法名为带异常值拒绝的压缩恢复（compressive recovery with outlier rejection, CORe），将测量数据中的异常值建模为加性辅助变量。我们对该辅助变量施加MR物理引导的组稀疏约束，并通过迭代算法将其与图像联合估计。为进行评估，首先通过两项仿真研究将CORe与传统压缩感知（compressed sensing, CS）、鲁棒回归（robust regression, RR）及一种现有的异常值拒绝方法进行对比；随后使用7个三维（3D）电影成像数据集、12个静息态四维（4D）血流成像数据集和8个负荷态4D血流成像数据集，将CORe与CS进行对比。结果：仿真研究显示，在55种不同实现场景下，CORe在归一化均方误差和结构相似性指数方面均优于CS、RR及现有异常值拒绝方法。专家对3D电影图像的阅片评估表明，CORe在抑制伪影方面更有效，同时可保持或提升图像清晰度。最后，4D血流图像显示CORe能产生更可靠、更一致的血流测量结果，尤其是在受试者存在不自主运动或运动负荷的情况下。结论：本文提出了一种异常值拒绝方法，并通过仿真数据和实测数据对其进行了测试。该方法可在多种自由运行式CMR应用中帮助抑制运动伪影。代码与数据：实现代码和数据集可在GitHub获取，地址为http://github.com/OSU-MR/motion-robust-CMR。
英文摘要：
PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion.
METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets.
RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress.
CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.
CODE & DATA: Implementation code and datasets are available on GitHub at http://github.com/OSU-MR/motion-robust-CMR
Darmstadt University of Technology（达姆施塔特工业大学）；Istanbul Technical University（伊斯坦布尔理工大学）
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisition quality and therefore subsequent analysis and final diagnosis. We present a workflow which predicts a severity score for respiratory motion in CMR for the CMRxMotion challenge 2022. This is an important tool for technicians to immediately provide feedback on the CMR quality during acquisition, as poor-quality images can directly be re-acquired …
Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-tissue contrast without the use of ionizing radiation. Physiological motions and limited speed of MRI data acquisition necessitate development of accelerated methods, which typically rely on undersampling. Recovering diagnostic quality CMR images from highly undersampled data has been an active area of research. Recently, several data acquisition and processing methods have been proposed to accelerate CMR. The availability of data to objectively evaluate and compare different reconstruction methods could e…
Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open da…
Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality that provides a comprehensive evaluation of the cardiovascular system. The clinical utility of CMR is hampered by long acquisition times, however. In this work, we propose and validate a plug-and-play (PnP) method for CMR reconstruction from undersampled multi-coil data. To fully exploit the rich image structure inherent in CMR, we pair the PnP framework with a deep learning (DL)-based denoiser that is trained using spatiotemporal patches from high-quality, breath-held cardiac cine images. The resulting "PnP-DL" method …
University of Leicester（莱斯特大学）；NIHR Leicester Biomedical Research Centre（NIHR莱斯特生物医学研究中心）；Glenfield Hospital（格伦菲尔德医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Saint-Petersburg State Electrotechnical University（圣彼得堡国立电工大学）
A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variabil…
Universidade Federal do Espírito Santo（圣埃斯皮里图联邦大学）；Instituto Federal do Espírito Santo（圣埃斯皮里图联邦理工学院）；University of Bern（伯尔尼大学）
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume MRI analysis increases, it becomes convenient to have each sequence well identified. However, the unstandardized naming of MRI sequences makes their identification difficult for automated systems, as well as makes it difficult for researches to generate or use datasets for machine learning research. In the face of that, we propose a system for identifying types of brain MRI sequences based on deep learning. By train…
Generalized super-resolution 4D Flow MRI $\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system
Karolinska Institutet（卡罗林斯卡学院）；University of Auckland（奥克兰大学）；University of Michigan（密歇根大学）；University of California San Francisco（加利福尼亚大学旧金山分校）；University of Greifswald（格赖夫斯瓦尔德大学）；Massachusetts Institute of Technology（麻省理工学院）
MIT（麻省理工学院）；Tsinghua University（清华大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI ob…
Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast enhanced (DCE) imaging. Theory and Methods: The problem considered here requires recovering hundred-gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three dimen…
Future Processing Healthcare（未来医疗处理公司）；Silesian University of Technology（西里西亚工业大学）；Medical University of Silesia in Katowice（卡托维兹西里西亚医科大学）；ZSM Chorzów（霍茹夫ZSM医院）；University Clinical Centre（大学临床中心）；Roche Pharmaceutical Research & Early Development（罗氏制药研究与早期开发部）
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimens…
University of Science and Technology Beijing（北京科技大学）；University of Central Florida（中佛罗里达大学）
For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice…
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nanyang Technological University（南洋理工大学）
University of Brescia（布雷西亚大学）；University of Glasgow（格拉斯哥大学）
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the t…
Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer va…
Wall shear stress (WSS) has been suggested as a potential biomarker in various cardiovascular diseases and it can be estimated from phase-contrast Magnetic Resonance Imaging (PC-MRI) velocity measurements. We present a new parametric sequential method for MRI-based WSS quantification consisting of a geometry identification and a subsequent approximation of the velocity field. The new method was validated on well controlled, high resolution in vitro measurements of turbulent stationary flows and physiological pulsatile flows in phantoms. Basic applicability to in vivo was demonstrated using 2D …
University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital, Bispebjerg and Frederiksberg（哥本哈根大学医院比斯佩布杰格和弗雷德里克斯堡院区）
UNet has become the gold standard method for segmenting 2D medical images that any new method must be validated against. However, in recent years, several variations of the seminal UNet have been proposed with promising results. However, there is no clear consensus on the generalisability of these architectures, and UNet currently remains the methodological gold standard. The purpose of this study was to evaluate some of the most promising UNet-inspired architectures for 3D segmentation. For the segmentation of 3D scans, UNet-inspired methods are also dominant, but there is a larger variety ac…
SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging
School of Engineering, College of Engineering and Physical Sciences（工程学院，工程与物理科学学院）；University of Birmingham（伯明翰大学）；Oxford Center for Clinical Magnetic Resonance Research (OCMR)（牛津临床磁共振研究中心）；University of Oxford（牛津大学）；John Radcliffe Hospital（约翰·拉德克利夫医院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Queen Mary University London（伦敦女王大学）；Barts Health NHS Trust（巴特勒医院 NHS 信托）
Fluid intelligence (Gf) has been defined as the ability to reason and solve previously unseen problems. Links to Gf have been found in magnetic resonance imaging (MRI) sequences such as functional MRI and diffusion tensor imaging. As part of the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019, we sought to predict Gf in children aged 9-10 from T1-weighted (T1W) MRIs. The data included atlas-aligned volumetric T1W images, atlas-defined segmented regions, age, and sex for 3739 subjects used for training and internal validation and 415 subjects used for external va…
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…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Institute for Signals, Systems, and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) CONICET-UNL）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds（生物医学计算成像与模拟技术中心（CISTIB），计算学院，莱斯特大学）；Christabel Pankhurst Institute, The University of Manchester（克里斯特贝尔·潘克赫斯特研究所，曼彻斯特大学）；Centre for Computational Imaging and Modelling in Medicine (CIMIM), Department of Computer Science, School of Engineering, and Division of Informatics Imaging and Data Science, School of Health Sciences, The University of Manchester（医学计算成像与建模中心（CIMIM），计算机科学系，工程学院，信息学成像与数据科学系，健康科学学院，曼彻斯特大学）；Medical Imaging Research Centre (MIRC), Department of Cardiovascular Sciences, KU Leuven（医学影像研究中心（MIRC），心血管科学系，鲁汶大学）
A. K. Choudhury School of Information Technology, University of Calcutta（加尔各答大学A.K.乔杜里信息技术学院）；Indian Statistical Institute（印度统计研究所）
Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain along a single axis, and (ii) generation of missing inter-slice data are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. The sequence of original 2D …
City College New York（纽约市立学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead spatial patterns and can learn to segment lesions, but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional convolutional network, namely, spatial priors with a TPM, morphological priors with conditional rand…
University of Michigan（密歇根大学）；Izmir University of Economics（伊兹密尔经济大学）；Ege University School of Medicine（埃格大学医学院）
Purpose; The purpose of this study is to classify glial tumors into grade II, III and IV categories noninvasively by application of machine learning to multi-modal MRI features in comparison with volumetric analysis. Methods; We retrospectively studied 57 glioma patients with pre and postcontrast T1 weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The tumors were segmented into enhancing and nonenhancing portions, tumor necrosis, cyst and edema using semiautomated segmentation of ITK-SNAP open source tool. We measured total tumor volume, enhancing-nonenhancing tumor, ede…
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction
Department of Bioengineering and Imperial-X, Imperial College London（生物工程系和Imperial-X，帝国理工学院伦敦分校）；Department of Bioengineering, Imperial College London（生物工程系，帝国理工学院伦敦分校）
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructi…
National University of Cordoba（科尔多瓦国立大学）；Technical University of Munich（慕尼黑工业大学）；University of Burgundy（勃艮第大学）；University Hospital of Dijon（第戎大学医院）；Catholic University of Cordoba（科尔多瓦天主教大学）
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expert-annotated at six aortic locations typically used…
Velocity-encoded MRI is an imaging technique used in different areas to assess flow motion. Some applications include medical imaging such as cardiovascular blood flow studies, and industrial settings in the areas of rheology, pipe flows, and reactor hydrodynamics, where the goal is to characterise dynamic components of some quantity of interest. The problem of estimating velocities from such measurements is a nonlinear dynamic inverse problem. To retrieve time-dependent velocity information, careful mathematical modelling and appropriate regularisation is required. In this work, we propose an…
Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
The Grainger College of Engineering（格拉inger工程学院）；National Center for Supercomputing Applications（国家超级计算应用中心）；University of Illinois Urbana‑Champaign（伊利诺伊大学厄巴纳-香槟分校）
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achiev…
Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. Current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical…
Fully Automated Assessment of Cardiac Coverage in Cine Cardiovascular Magnetic Resonance Images using an Explainable Deep Visual Salient Region Detection Model
Purpose: To enable rigid-body motion tolerant parallel volumetric magnetic resonance imaging by retrospective head motion correction on a variety of spatio-temporal scales and imaging sequences. Theory and methods: Tolerance against rigid-body motion is based on distributed and incoherent sampling orders for boosting a joint retrospective motion estimation and reconstruction framework. Motion resilience stems from the encoding redundancy in the data, as generally provided by the coil array. Hence, it does not require external sensors, navigators or training data, so the methodology is readily …
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；University of California, Los Angeles（加州大学洛杉矶分校）
Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a…
Deep transfer learning using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown strong predictive power in characterization of breast lesions. However, pretrained convolutional neural networks (CNNs) require 2D inputs, limiting the ability to exploit the rich 4D (volumetric and temporal) image information inherent in DCE-MRI that is clinically valuable for lesion assessment. Training 3D CNNs from scratch, a common method to utilize high-dimensional information in medical images, is computationally expensive and is not best suited for moderately sized healthcare datasets. …
A Generalizable Deep Learning System for Cardiac MRI
Division of Cardiovascular Surgery, Department of Surgery, University of Pennsylvania（宾夕法尼亚大学心血管外科 division）；Department of Cardiothoracic Surgery, Stanford University（斯坦福大学心胸外科 department）；Department of Radiology, Medstar Georgetown University Hospital（梅奥医疗中心乔治城大学医院放射科 department）；Department of Radiology and Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科和生物医学成像 department）；Bunkerhill Health, San Francisco（旧金山布纳克希尔健康机构）；Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射科 department）；Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania（宾夕法尼亚大学心血管医学 division）；Division of Cardiovascular Medicine, Department of Medicine, Genetics, and Biomedical Data Science, Stanford University（斯坦福大学心血管医学 division）；Department of Radiology, Medicine, and Biomedical Data Science, Stanford University（斯坦福大学放射科、医学和生物医学数据科学 department）
Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）；The Cardiac Electro-Mechanics Research Group, Imperial College London（伦敦帝国理工学院心脏电机械学研究组）；The Alan Turing Institute, London（伦敦艾伦·图灵研究所）；Department of Cardiology, University Medical Center Utrecht（乌得勒支大学医学中心心内科部）；ETH Zurich（苏黎世联邦理工学院）
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Segmentation of cardiac fibrosis and scar are essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities, balanced steady-state free precession (bSSFP) and cine magnetic resonance imaging (MRI) for example, can further en…
With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing thi…
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
Department of Neurology, Medical University of Graz（格拉茨医科大学神经科）；Institute of Biomedical Imaging, Graz University of Technology（格拉茨技术大学生物医学成像研究所）；BioTechMed-Graz（格拉茨BioTechMed）
An Arbitrary-Modal Fusion Network for Volumetric Cranial Nerves Tract Segmentation
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Department of Chemical and Biological Engineering and Center for Aging Science , Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province（陕西核工业215医院神经外科）；Department of Neurosurgery, Taihe Hospital of Wannan Medical College（皖南医学院太和医院神经外科）
University College Dublin（都柏林大学学院）；Université Paris-Est Créteil Val de Marne - Université Paris 12（巴黎东-马恩拉瓦莱大学（巴黎第十二大学））；ADAPT SFI Research Centre（ADAPT SFI研究中心）
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Ne…
Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation
Shenzhen University（深圳大学）；Shenzhen University Medical School（深圳大学医学院）；School of Biomedical Engineering（生物医学工程学院）；Huawei Cloud Computing Technologies Company Limited（华为云计算技术有限公司）；Shenzhen University General Hospital（深圳大学总医院）
In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool) and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground truth segmentation data. Based on these results,…
An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion
Higher Institute of Medical Technologies of Tunis（突尼斯高等医疗技术学院）；University of Tunis El Manar（突尼斯埃尔马纳尔大学）；Faculty of Medicine of Monastir（莫纳斯提尔医学院）；University of Monastir（莫纳斯提尔大学）；Artificial Intelligence Research Centre, Ajman University（阿治曼大学人工智能研究中心）；Ajman University（阿治曼大学）；National Engineering School of Tunis（突尼斯国立工程师学院）；IPEIEM（突尼斯高等预科工程与技术学院（IPEIEM））
