Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco（加利福尼亚大学旧金山分校放射与生物医学影像系智能成像中心）；Department of Bioengineering, University of California, Berkeley（加利福尼亚大学伯克利分校生物工程系）；Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco（加利福尼亚大学旧金山分校生物工程与治疗科学系）；Department of Medical Sciences and Technology, Indian Institute of Technology, Madras（印度理工学院马德拉斯分校医学科学与技术系）；Bay Area Institute of Computation, Altos Labs（Altos Labs湾区计算研究所）
Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.
Imaging Biomarkers for Neurodegenerative Diseases from Detailed Segmentation of Medial Temporal Lobe Subregions on in vivo Brain MRI Using Upsampling Strategy Guided by High-resolution ex vivo MRI
University of Pennsylvania（宾夕法尼亚大学）；Siemens Healthineers（西门子医疗）；Lund University（隆德大学）；University of Castilla La Mancha（卡斯蒂利亚-拉曼恰大学）
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use nonlinear tensor registration to align the atlas to a subject and to estimate the error of this alignment. …
BioFace3D: A fully automatic pipeline for facial biomarkers extraction of 3D face reconstructions segmented from MRI
La Salle - Universitat Ramon Llull（拉蒙·柳利大学拉萨尔学院）；Universitat de Barcelona（巴塞罗那大学）；FIDMAG, Sisters Hospitallers Research Foundation（仁爱修女会研究基金会FIDMAG研究所）；CIBERSAM（精神健康生物医学研究网络）；Hospital de Sant Pau i la Santa Creu（圣保禄圣十字医院）
Sharif University of Technology（谢里夫理工大学）；Iranian Brain Mapping Biobank（伊朗脑图谱生物库）；National Brain Mapping Laboratory（国家脑图谱实验室）；Virtual University of Medical Sciences（虚拟医科大学）
Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease. Imaging Biomarkers of dementia are multiple and comprehensive documentation of them is time-consuming. Therefore, any effort to automatically extract these biomarkers will be of clinical value while reducing inter-rater discrepancies. To tackle this problem, we propose to classify the disease severity based on the Fazekas scale through …
Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern an…
Beihang University（北京航空航天大学）；University of Sydney（悉尼大学）；Beijing TianTan Hospital（北京天坛医院）；University of New South Wales（新南威尔士大学）
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain a…
Indian Statistical Institute（印度统计学院）；University of South Florida（南佛罗里达大学）
Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features, which does not consider either non-biological factors or the functional neurological status of the patient(s). Besides, the selection of an appropriate cut-off for survival and the presence of censored data create further problems. Application of deep learning models for OS prediction is also limited due to the lack of large annotated publicly available datasets. In this scenario we analyse th…
Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers
organization= Shanghai Artificial Intelligence Laboratory , city= Shanghai , postcode= 200082 , country= China；organization= School of Medicine, Xiamen University , city= Xiamen , state= Fujian , country= China；organization= Department of Nuclear Medicine \& PET Center, Huashan Hospital, Fudan University , city= Shanghai , country= China；organization= Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital , city= Shanghai , country= China；organization= Department of Computer Science, Rutgers University , city= New Brunswick , state= New Jersey , country= United States；organization= Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , city= Shenzhen , country= China
Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating Radiographic Feature Information
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University Sorbonne Paris Nord（巴黎北大索邦大学）；German Cancer Research Center（德国癌症研究中心）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；Henan University of Chinese Medicine（河南中医药大学）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
Erasmus MC（伊拉斯谟医学中心）；Delft University of Technology（代尔夫特理工大学）；University of Copenhagen（哥本哈根大学）
To measure the volume of specific image structures, a typical approach is to first segment those structures using a neural network trained on voxel-wise (strong) labels and subsequently compute the volume from the segmentation. A more straightforward approach would be to predict the volume directly using a neural network based regression approach, trained on image-level (weak) labels indicating volume. In this article, we compared networks optimized with weak and strong labels, and study their ability to generalize to other datasets. We experimented with white matter hyperintensity (WMH) volum…
Tel Aviv University（特拉维夫大学）；Sheba Medical Center（舍巴医疗中心）；Sackler School of Medicine（萨克勒医学院）
Magnetic Resonance Imaging (MRI) is a widely-accepted imaging technique for knee injury analysis. Its advantage of capturing knee structure in three dimensions makes it the ideal tool for radiologists to locate potential tears in the knee. In order to better confront the ever growing workload of musculoskeletal (MSK) radiologists, automated tools for patients' triage are becoming a real need, reducing delays in the reading of pathological cases. In this work, we present the Efficiently-Layered Network (ELNet), a convolutional neural network (CNN) architecture optimized for the task of initial …
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）
In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to synthesize arbitrary MRI contrasts. Estimating tissue properties from a single scan session using a protocol available on all clinical scanners promises to reduce scan time and cost, enable quantitative analysis in routine clinical scans and provide scan-independent biomarkers of disease. However, existing tissue properties estimation methods - most often $\mathbf{T_1}$ relaxation, $\mathbf{T_2^*}$ relaxation, and proto…
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）；Prince of Wales Hospital（威尔士亲王医院）
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Se…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Radboud University Medical Centre（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Macao Polytechnic University（澳门理工大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
Clinical Graph-Mediated Distillation for Unpaired MRI-to-CFI Hypertension Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Research Convergence Institute（研究融合院）；Department of AI Systems Engineering（人工智能系统工程系）；Department of Electrical and Computer Engineering（电气与计算机工程系）
A multimodal vision foundation model for generalizable knee pathology
Beihang University（北京航空航天大学）；Peking University Third Hospital（北京大学第三医院）；Institute of Sports Medicine of Peking University（北京大学运动医学研究所）；Ministry of Education（教育部）
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医疗技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics GmbH公司）；Hamburg University of Technology（汉堡工业大学）
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with …
University of Surrey（萨里大学）；Children’s National Hospital（国家儿童医院）；Surrey Institute for People-Centred AI（萨里以人为本人工智能研究所）
The availability of large scale data with high quality ground truth labels is a challenge when developing supervised machine learning solutions for healthcare domain. Although, the amount of digital data in clinical workflows is increasing, most of this data is distributed on clinical sites and protected to ensure patient privacy. Radiological readings and dealing with large-scale clinical data puts a significant burden on the available resources, and this is where machine learning and artificial intelligence play a pivotal role. Magnetic Resonance Imaging (MRI) for musculoskeletal (MSK) diagn…
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clini…
Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI
Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite…
University of Iceland（冰岛大学）；Landspitali - University Hospital（兰斯皮塔利大学医院）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are common in the brains of the elderly population and may be caused by ischemia or demyelination. Lesions are biomarkers for various neurodegenerative diseases, making accurate quantification of them important for both disease diagnosis and progression. Automatic lesion detection using supervised learning requires manually annotated images, which can often be impractical to acquire. Unsupervised lesion detection, on the other hand, does not r…
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which…
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propos…
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this work we introduce a convolutional neural network model for identifying disease in temporal sequences of cardiac MR segmentations which is interpretable in terms of clinically familiar measurements. The model is based around a variational autoencoder, reducing the input into a low-dimensional latent space in which classification occurs. We then use the recently developed `conce…
University of Warsaw（华沙大学）；Warsaw University of Technology（华沙理工大学）；Medical University of Warsaw（华沙医科大学）；Carolina Medical Center（卡罗莱纳医疗中心）；Tooploox
Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorder in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts. In this paper, we propose to significantly reduce the complexity of this assessment using a novel method based on a pre-trained convolutional neural network. We first train our neural network on over 500,000 2D axial cross-sections from over 3000 3D MRI studies to classify MRI images as belonging to a healthy or i…
Uppsala University（乌普萨拉大学）；Antaros Medical AB（安塔罗斯医疗公司）
Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata, anatomical information from these images has the potential for medical analyses at unprecedented scale. However, their evaluation often requires manual input and long processing times, limiting the amount of reference values for biomarkers and other measurements available for research. Recent approaches with convolutional neural networks for regression can perform these evaluations automatically. On magnetic resonan…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Incyte Corporation（因赛特公司）
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter- and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to intro…
Ege University（埃杰大学）；University of Central Florida（中佛罗里达大学）；University of Pennsylvania（宾夕法尼亚大学）
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and stu…
Effective and accurate diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI) can be critical for early treatment and thus has attracted more and more attention nowadays. Since first introduced, machine learning methods have been gaining increasing popularity for AD related research. Among the various identified biomarkers, magnetic resonance imaging (MRI) are widely used for the prediction of AD or MCI. However, before a machine learning algorithm can be applied, image features need to be extracted to represent the MRI images. While good representations can be pivotal to the…
Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an unsupervised deep-neural-network architecture for motion compensated quantitative DWI (qDWI) analysis. Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network. We simultaneously estimate the qDWI param…
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
Taipei Medical University（台北医学大学）；University of Memphis（孟菲斯大学）；Chitkara University（奇特卡拉大学）；University College London（伦敦大学学院）；UPES（石油与能源研究大学）；University of Eastern Finland（东芬兰大学）；CNRS（法国国家科学研究中心）；University Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as …
