SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
Duke（杜克大学）
Hanxue Gu, Roy Colglazier, Haoyu Dong, Jikai Zhang, Yaqian Chen, Zafer Yildiz, Yuwen Chen, Lin Li, Jichen Yang, Jay Willhite, Alex M. Meyer, Brian Guo, Yashvi A… 展开作者
Hanxue Gu, Roy Colglazier, Haoyu Dong, Jikai Zhang, Yaqian Chen, Zafer Yildiz, Yuwen Chen, Lin Li, Jichen Yang, Jay Willhite, Alex M. Meyer, Brian Guo, Yashvi Atul Shah, Emily Luo, Shipra Rajput, Sally Kuehn, Clark Bulleit, Kevin A. Wu, Jisoo Lee, Brandon Ramirez, Darui Lu, Jay M. Levin, Maciej A. Mazurowski
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of MRIs into different organs and tissues would be highly beneficial since it would allow for a higher level of understanding of the image content and enable important measurements, which are essential for accurate diagnosis and effective treatment planning. Specifically, segmenting bones in MRI would allow for more quantitative assessments of musculoskeletal conditions, while such assessments are largely absent in current radiological practice. The difficulty of bone MRI segmentation is illustrated by the fact that limited algorithms are publicly available for use, and those contained in the literature typically address a specific anatomic area. In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations. The proposed model can operate in two modes: fully automated segmentation and prompt-based segmentation. Our contributions include (1) collecting and annotating a new MRI dataset across various MRI protocols, encompassing over 300 annotated volumes and 8485 annotated slices across diverse anatomic regions; (2) investigating several standard network architectures and strategies for automated segmentation; (3) introducing SegmentAnyBone, an innovative foundational model-based approach that extends Segment Anything Model (SAM); (4) comparative analysis of our algorithm and previous approaches; and (5) generalization analysis of our algorithm across different anatomical locations and MRI sequences, as well as an external dataset. We publicly release our model at https://github.com/mazurowski-lab/SegmentAnyBone.
Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China（计算机科学与技术学院，广东技术大学，广州，中国）；Neurological Institute, Cleveland Clinic, OH, USA（神经医学研究院，克利夫兰医学中心，俄亥俄州，美国）；Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China（神经外科，中山大学附属第一医院，广州，中国）；Brigham and Women’s Hospital, Harvard Medical School, MA, USA（布里奇沃特医院，哈佛医学院，马萨诸塞州，美国）；Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands（放射科，医学影像中心，格罗宁根大学医学中心，格罗宁根大学，荷兰）
École de Technologie Supérieure（高等技术学院）；CHU Sainte-Justine Research Center（圣茹斯特医院研究中心）；Polytechnique Montréal（蒙特利尔理工学院）
Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of pai…
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）
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
GE Healthcare（通用电气医疗集团）；Umeå University（于默奥大学）；Erasmus MC Cancer Institute（伊拉斯谟医学中心癌症研究所）；Newcastle University（纽卡斯尔大学）；Newcastle upon Tyne Hospitals NHS Foundation Trust（泰恩河畔纽卡斯尔NHS基金会信托）；Technical University of Munich（慕尼黑工业大学）；Sree Chitra Tirunal Institute of Medical Sciences and Technology（斯里奇特拉蒂鲁纳尔医学科学与技术研究所）；University of Zurich（苏黎世大学）
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localisation of region of interest (RoI) via classification, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized…
Ibn Tofail University（伊本·托法勒大学）；Cheikh Anta Diop University（谢赫·安塔·迪奥普大学）；Abdelmalek Essaadi University（阿卜杜勒马利克·埃萨迪大学）
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potentia…
Iowa Institute for Biomedical Imaging（爱荷华生物医学影像研究所）；The University of Iowa（爱荷华大学）
State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA). We present a novel interactive method called just-enough interaction (JEI), which adds a fast correction step to the automated layered optimal graph segmentation of multiple objects and surfaces (LOGISMOS). After LOGISMOS segmentation in knee MRI, the JEI user interaction does not modify boundary surfaces of the bones and cartilages directly. Local costs of underlying graph nodes are modified instead and the graph is re…
As bone and air produce weak signals with conventional MR sequences, segmentation of these tissues particularly difficult in MRI. We propose to integrate patch-based anatomical signatures and an auto-context model into a machine learning framework to iteratively segment MRI into air, bone and soft tissue. The proposed semantic classification random forest (SCRF) method consists of a training stage and a segmentation stage. During training stage, patch-based anatomical features were extracted from registered MRI-CT training images, and the most informative features were identified to train a se…
Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像中心）；Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；School of Psychological Sciences, Monash University（莫纳什大学心理科学学院）；Department of Data Science and AI, Monash University（莫纳什大学数据科学与人工智能系）
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI) scans as a result of close-range proton density and T1/T2 parameters. Furthermore, to achieve accelerated MRI, k-space data are undersampled which causes global aliasing artifacts. Convolutional neural network (CNN) models are widely utilized for accelerated MRI reconstruction, but those models are limited in capturing global correlations due to the intrinsic locality of the convolution operation. The self-attention-…
Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）；Hacettepe University（哈塞特佩大学）
In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train an image segmentation engine. We showed promising segmentation results tested on real multi-contrast MRI scans when delineating muscle, fat, bone and bone marrow, all trained on synthetic images. Based on synthetic image training, our segmentation results were as high as 93.91\%, 94.11\%, 91.63\%, 95.33\%, for muscle, fat, bone, and bone marrow delineation, respectively. Results were not significantly different from…
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）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；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）；University of Ottawa（渥太华大学）；Children's Hospital of Eastern Ontario（东安大略儿童医院）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements…
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
Gordon Center for Medical Imaging, Harvard Medical School and Massachusetts General Hospital（哈佛医学院与麻省总医院戈登医学成像中心）；Boston University（波士顿大学）；Seidenberg School of CSIS, Pace University（佩斯大学塞登伯格计算机科学与信息系统学院）；Ewha Womans University（梨花女子大学）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Orthopedics, University Medical Center Utrecht（乌得勒支大学医学中心骨科）；MRIguidance B.V.（MRIguidance有限公司（乌得勒支））；University Medical Center Utrecht（乌得勒支大学医学中心）
Purpose: To assess the feasibility of deep learning-based high resolution synthetic CT generation from MRI scans of the lower arm for orthopedic applications. Methods: A conditional Generative Adversarial Network was trained to synthesize CT images from multi-echo MR images. A training set of MRI and CT scans of 9 ex vivo lower arms was acquired and the CT images were registered to the MRI images. Three-fold cross-validation was applied to generate independent results for the entire dataset. The synthetic CT images were quantitatively evaluated with the mean absolute error metric, and Dice sim…
Purpose: The purpose of our study was to use Dual-TR STE-MR protocol as a clinical tool for cortical bone free water quantification at 1.5T and validate it by comparing the obtained results (MR-derived results) with dehydration results. Methods: Human studies were compliant with HIPPA and were approved by the institutional review board. Short Echo Time (STE) MR imaging with different Repetition Times (TRs) was used for quantification of cortical bone free water T1 (T1free) and concentration (\r{ho}free). The proposed strategy was compared with the dehydration technique in seven bovine cortical…
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
Center Algoritmi / LASI（中心算法/拉斯伊）；Institute for AI in Medicine (IKIM)（人工智能医学研究所）；Institute of Medical Informatics（医学信息研究所）；Department of Oral and Maxillofacial Surgery（口腔和颅面外科部门）；Department of Diagnostic and Interventional Radiology（诊断和介入放射科部门）；Life and Health Sciences Research Institute (ICVS), School of Medicine（生命与健康科学研究所（ICVS），医学院）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心（CCCE））
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and speciall…
Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of medical images. Though closely related to semantic segmentation, medical image segmentation includes specific challenges that need to be addressed, such as the scarcity of labelled data, the high class imb…
Computed tomography (CT) is a widely used imaging modality for medical diagnosis and treatment. In electroencephalography (EEG), CT imaging is necessary for co-registering with magnetic resonance imaging (MRI) and for creating more accurate head models for the brain electrical activity due to better representation of bone anatomy. Unfortunately, CT imaging exposes patients to potentially harmful sources of ionizing radiation. Image synthesis methods present a solution for avoiding extra radiation exposure. In this paper, we perform image synthesis to create a realistic, synthetic CT image from…
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨骼与关节健康研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (AMII)（阿尔伯塔机器智能研究所）
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
Department of Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学）；Department of Biostatistics and Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Biostatistics and Bioinformatics, Radiology, Electrical and Computer Engineering, and Computer Science, Duke University（生物统计学与生物信息学系，放射学，电气与计算机工程，和计算机科学，杜克大学）
本文提出 SLM-SAM 2 架构，通过分离的短期和长期记忆库及注意力模块解决 SAM 2 在体积医学图像标注中的错误传播问题，在四个数据集上显著提升了分割精度并减少了 60.575% 的掩码校正时间。
Schulich School of Engineering, University of Calgary（卡尔加里大学舒立克工程学院）；Department of Radiology & Diagnostic Imaging, University of Alberta（阿尔伯塔大学放射与诊断成像系）；Alberta Machine Intelligence Institute (AMII), University of Alberta（阿尔伯塔大学阿尔伯塔机器智能研究所（AMII））；Mechanical and Manufacturing Engineering, University of Calgary（卡尔加里大学机械与制造工程系）
Objective assessment of Magnetic Resonance Imaging (MRI) scans of osteoarthritis (OA) can address the limitation of the current OA assessment. Segmentation of bone, cartilage, and joint fluid is necessary for the OA objective assessment. Most of the proposed segmentation methods are not performing instance segmentation and suffer from class imbalance problems. This study deployed Mask R-CNN instance segmentation and improved it (improved-Mask R-CNN (iMaskRCNN)) to obtain a more accurate generalized segmentation for OA-associated tissues. Training and validation of the method were performed usi…
Short inversion time inversion recovery (STIR) MRI is widely used in clinical practice to identify and quantify inflammation in axial spondyloarthritis. However, assessment of STIR images is limited by the need for qualitative evaluation, which depends on observer experience and expertise, creating substantial variability in inflammation assessments. To address this problem, we developed a deep learning-enabled, semiautomated workflow for segmentation of inflammatory lesions, whereby an initial segmentation is generated automatically and a radiologist then 'cleans' the segmentation by removing…
Generalizing imaging biomarker repeatability studies using Bayesian inference: Applications in detecting heterogeneous treatment response in whole-body diffusion-weighted MRI of metastatic prostate cancer
提出一种基于 Hamiltonian Monte Carlo 的贝叶斯框架，以量化不同分布假设下影像异质性生物标志物的测量不确定性，并在 mCRPC 的 WBDWI 数据中验证其疗效评估价值。
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
TUM University Hospital（慕尼黑工业大学附属医院）；Technical University of Munich（慕尼黑工业大学）；University Medicine Greifswald（格赖夫斯瓦尔德大学医学中心）；University Medical Center Freiburg（弗赖堡大学医学中心）；University of Freiburg（弗赖堡大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Imperial College London（伦敦帝国理工学院）
