Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
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）
Camilo Bermudez, Justin Blaber, Samuel W. Remedios, Jess E. Reynolds, Catherine Lebel, Maureen McHugo, Stephan Heckers, Yuankai Huo, Bennett A. Landman
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 degradation of performance on the original validation/test cohorts. Here, we explore TL by data augmentation to address these concerns in the context of adapting SLANT to anatomical variation and scanning protocol. We consider two datasets: First, we optimize for age with 30 T1w MRI of young children with manually corrected volumetric labels, and accuracy of automated segmentation defined relative to the manually provided truth. Second, we optimize for acquisition with 36 paired datasets of pre- and post-contrast clinically acquired T1w MRI, and accuracy of the post-contrast segmentations assessed relative to the pre-contrast automated assessment. For both studies, we augment the original TL step of SLANT with either only the new data or with both original and new data. Over baseline SLANT, both approaches yielded significantly improved performance (signed rank tests; pediatric: 0.89 vs. 0.82 DSC, p<0.001; contrast: 0.80 vs 0.76, p<0.001). The performance on the original test set decreased with the new-data only transfer learning approach, so data augmentation was superior to strict transfer learning.
University of Ottawa（渥太华大学）；Children’s Hospital of Eastern Ontario（东安大略儿童医院）
Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) with PMG and controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG MRIs and control MRIs are subtle and the true distribution of the features of the disease is unknown. This mak…
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Hospital Augsburg（奥格斯堡大学医院）
University of São Paulo（圣保罗大学）；Instituto da Criança – HC-FM（儿童研究所——医学院附属医院）
The importance of imaging exams, such as Magnetic Resonance Imaging (MRI), for the diagnostic and follow-up of pediatric pathologies and the assessment of anatomical structures' development has been increasingly highlighted in recent times. Manual analysis of MRIs is time-consuming, subjective, and requires significant expertise. To mitigate this, automatic techniques are necessary. Most techniques focus on adult subjects, while pediatric MRI has specific challenges such as the ongoing anatomical and histological changes related to normal development of the organs, reduced signal-to-noise rati…
University of Toronto（多伦多大学）；The Hospital for Sick Children（SickKids医院（多伦多儿童医院））；Vector Institute（向量研究所）
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities. While previous generative modeling approaches successfully perform anomaly detection by learning the distribution of healthy 2D image slices, they process such slices independently and ignore the fact that they are correlated, all being sampled from a 3D volume. We show that incorporating the 3D context and processing whole-body MRI volumes is beneficial to distinguishing anomalies from their benign counterparts. In our work, we introduce a multi-channel sli…
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
TinyViT-Batten: Few-Shot Vision Transformer with Explainable Attention for Early Batten-Disease Detection on Pediatric MRI
Department of Applied Medical Sciences（应用医学科学系）；RareGen Youth Network 501(c)(3)（RareGen青年网络501(c)(3)）；Harvard University（哈佛大学）；Carnegie Mellon University（卡内基梅隆大学）；Mellon College of Science（梅隆科学学院）
Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023
Princess Máxima Center for Pediatric Oncology（玛西玛公主儿科肿瘤中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）；Osaka University（大阪大学）；Muroran Institute of Technology（室兰工业大学）；Niigata University of Health and Welfare（新潟医疗福祉大学）；AGH University of Krakow（克拉科夫AGH科技大学）；HES-SO Valais-Wallis（瑞士西部应用科学与艺术大学瓦莱分校）；University of Geneva（日内瓦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；South China Normal University（华南师范大学）；La Fe Health Research Institute（拉费健康研究所）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Wageningen Food Safety Research（瓦赫宁根食品安全研究所）
Pediatric Pancreas Segmentation from MRI Scans with Deep Learning
Northwestern University（西北大学）；Gazi University Faculty of Medicine（加齐大学医学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；University of Iowa, Carver College of Medicine（爱荷华大学卡弗医学院）
ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
Kwame Nkrumah University of Science and Technology（科拉努姆大学科学与技术学院）；University of Catania（卡塔尼亚大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Medical Artificial Intelligence Lab（医学人工智能实验室）
HyKid: An Open MRI Dataset with Expert-Annotated Multi-Structure and Choroid Plexus in Pediatric Hydrocephalus
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China（浙江大学生物医学工程与仪器科学学院）；Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China（浙江大学医学院儿童医院放射科）；Neurosurgery of Zhejiang Hospital, Hangzhou, China（浙江医院神经外科）
MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA（Sheikh Zayed儿童外科创新研究所，Children’s National医院，华盛顿特区，美国）；CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA（CIBORG实验室，放射科，Children’s Hospital Los Angeles，洛杉矶，加利福尼亚州，美国）；Departments of Radiology and Biomedical Engineering, University of Southern California, Los Angeles, CA, USA（放射科和生物医学工程系，美国南加州大学，洛杉矶，加利福尼亚州，美国）；School of Medicine and Health Sciences, George Washington University, Washington, DC, USA（医学与健康科学学院，乔治华盛顿大学，华盛顿特区，美国）
Asymmetric Paired-Annotation Learning for Multi-Structure ULF Pediatric Brain MRI Segmentation
VinUniversity；College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Hanoi University of Science and Technology（河内科技大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）；Carnegie Mellon University（卡内基梅隆大学）；Northwestern University（西北大学）；Center for Innovations in Health Sciences, VinUniversity（VinUniversity健康科学创新中心）
针对0.064 T ULF儿科脑部MRI分割难题，提出基于nnU-Net的非对称监督策略AURA，利用两类非等效标注训练，在16例数据上取得优于基线的分割性能。
University of Iowa（爱荷华大学）；Philips HealthCare（飞利浦医疗）；University of Wisconsin（威斯康星大学）；GE HealthCare（通用电气医疗）
Free-breathing cardiac MRI schemes are emerging as competitive alternatives to breath-held cine MRI protocols, enabling applicability to pediatric and other population groups that cannot hold their breath. Because the data from the slices are acquired sequentially, the cardiac/respiratory motion patterns may be different for each slice; current free-breathing approaches perform independent recovery of each slice. In addition to not being able to exploit the inter-slice redundancies, manual intervention or sophisticated post-processing methods are needed to align the images post-recovery for qu…
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Medical AI, Shenzhen Institute 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中心）；Shenzhen Children's Hospital（深圳市儿童医院）
The Chinese University of Hong Kong（香港中文大学）；Chinese Academy of Sciences（中国科学院）
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and m…
Northeastern University（东北大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal informatio…
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance…
Institute of Signal Processing and System Theory, University of Stuttgart（斯图加特大学信号处理与系统理论研究所）；Visualization Research Center, University of Stuttgart（斯图加特大学可视化研究中心）；Department of Diagnostic and Interventional Radiology, University Hospital Tübingen（蒂宾根大学医院诊断与介入放射科）；Empirical Inference Department, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所实证推理部）
The concept of biological age (BA), although important in clinical practice, is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessmen…
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled traini…
Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated three-dimensional (3D) volume sampling over cardiac phases and breathing cycles necessitate accelerated imaging techniques that leverage data correlations. Standard compressed sensing reconstruction methods require tuning of hyperparameters and are computationally expensive, which diminishes the potential reduction of examination times. We propose an efficient model-based deep neural reconstruction network and eval…
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
Universitat Politècnica de Catalunya (UPC)（西班牙巴塞罗那理工大学）；University College Dublin (UCD)（都柏林大学）；Dublin City University (DCU)（都柏林城市大学）；Insight Research Ireland Center For Data Analytics（爱尔兰数据分析研究中心）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Medical Center（特拉维夫医疗中心萨戈尔脑研究所）；Tel Aviv Medical Center（特拉维夫医疗中心）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive or depleted adiposity. Although previous studies showed a correlation between the amount of AT and perinatal outcome, prenatal assessment of AT is limited by lacking quantitative methods. Using magnetic resonance imaging (MRI), 3D fat- and water-only images of the entire fetus can be obtained from two point Dixon images to enable AT lipid quantification. This paper is the first to present a methodology for developing …
IMT Atlantique（大西洋国立高等矿业电信学院）；Inserm（法国国家健康与医学研究院）；University Hospital of Brest（布雷斯特大学医院）；Fondation ILDYS（ILDYS基金会）；NIH（美国国立卫生研究院）
Automatic segmentation of pathological shoulder muscles in patients with musculo-skeletal diseases is a challenging task due to the huge variability in muscle shape, size, location, texture and injury. A reliable fully-automated segmentation method from magnetic resonance images could greatly help clinicians to plan therapeutic interventions and predict interventional outcomes while eliminating time consuming manual segmentation efforts. The purpose of this work is three-fold. First, we investigate the feasibility of pathological shoulder muscle segmentation using deep learning techniques, giv…
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI·维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach…
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Northwestern University（西北大学）；Istanbul University（伊斯坦布尔大学）；University of Catania（卡塔尼亚大学）；Erasmus Medical Center（伊拉斯姆斯医学中心）；University of Wisconsin（威斯康星大学）；Mayo Clinic（梅奥诊所）；New York University（纽约大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；NVIDIA（英伟达公司）
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Research center for Medical AI, 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）；Nanchang University（南昌大学）；University at Buffalo, the State University of New York（纽约州立大学布法罗分校）
Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation injury in CT and PET. Therefore, accelerating MRI, reducing radiation dose in CT and PET have been ongoing research topics since their invention. Usually, acquiring less data is a direct but important strategy to address these issues. However, less acquisition usually results in aliasing artifacts in reconstructions. Recently, deep learning (DL) has been introduced in medical image reconstruction and shown potential…
CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands（莱顿大学医学中心放射科）；Philips Innovative Technologies, Hamburg, Germany（飞利浦创新技术）
CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
Department of Computer Science and Software Engineering, Concordia University（计算机科学与软件工程系，康科迪亚大学）；Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）
Automated ensemble method for pediatric brain tumor segmentation
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Institute for Applied Life Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校应用生命科学研究所）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝塞特校区）；Baystate Medical Center（贝塞特医疗中心）
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI and Helmholtz Center Munich（亥姆霍兹人工智能与亥姆霍兹慕尼黑中心）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）
University of Catania（卡塔尼亚大学）；Northwestern University（西北大学）；Mayo Clinic（梅奥诊所）；University of Torino（都灵大学）
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established prog…
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framewor…
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Children's Foundation Research Institute at LeBonheur Children's Hospital（勒邦赫儿童医院儿童基金会研究所）；LeBonheur Children's Hospital（勒邦赫儿童医院）；University of Tennessee School of Medicine（田纳西大学医学院）
The ability to determine if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size characteristics in determining if brain maturity matches the chronological age of the patient, this requires years of experience with pediatric neuroradiology. Due to the lack of standardized criteria, estimation of brain maturity before age three remains fraught with interob…
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Artificial intelligence (AI) in radiology has made great strides in recent years, but many hurdles remain. Overfitting and lack of generalizability represent important ongoing challenges hindering accurate and dependable clinical deployment. If AI algorithms can avoid overfitting and achieve true generalizability, they can go from the research realm to the forefront of clinical work. Recently, small data AI approaches such as deep neuroevolution (DNE) have avoided overfitting small training sets. We seek to address both overfitting and generalizability by applying DNE to a virtually pooled dat…
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks
The Hospital for Sick Children (SickKids)（病童医院 (SickKids)）；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Vector Institute（Vector研究所）
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 …
