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 paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.
ShanghaiTech University（上海科技大学）；University of Alberta（阿尔伯塔大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Ultrasound spine imaging technique has been applied to the assessment of spine deformity. However, manual measurements of scoliotic angles on ultrasound images are time-consuming and heavily rely on raters experience. The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs. The framework consisted of two closely linked modules: 1) the lamina detector for identifying and locating each lamina pairs on ultrasound coronal images, and 2) the spinal curvature…
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Alberta（阿尔伯塔大学）
Objective: The spinous process angle (SPA) is one of the essential parameters to denote three-dimensional (3-D) deformity of spine. We propose an automatic segmentation method based on Stacked Hourglass Network (SHN) to detect the spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs of clinical scoliotic subjects. Methods: The network was trained to detect vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images and validated on 100 images. All the processed transverse images with highlighted SP and laminae were reconstructed into a 3D image vol…
A multi-agent system for spine MRI report generation from multi-sequence imaging
University of Washington（华盛顿大学）；Peking University（北京大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；New York University（纽约大学）；University of Washington Medical Center（华盛顿大学医学中心）
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI
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）；Rammohan College, University of Calcutta（加尔各答大学拉莫汉学院）；Indian Statistical Institute（印度统计研究院）
Technische Universität München（慕尼黑工业大学）；ScintHealth GmbH（ScintHealth有限公司）；Reply SpA（Reply股份公司）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能研究所）
CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achi…
SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI
NeuroPoly, Institute of Biomedical Engineering, Polytechnique Montreal（NeuroPoly，生物医学工程研究所，蒙特利尔大学）；Mila, Quebec AI Institute, Montreal（Mila，魁北克人工智能研究所，蒙特利尔）；Department of Radiology, Balgrist University Hospital, Zurich（放射科，巴尔格里斯大学医院，苏黎世）；Faculty of Medicine, University of Zurich（医学学院，苏黎世大学）；Department of Neurosurgery, Brain & Spine, University of South Florida（神经外科，脑与脊柱，佛罗里达大学）；Center for Neuromusculoskeletal Research, University of South Florida（神经肌肉研究中心，佛罗里达大学）；Department of Medical Engineering, University of South Florida（医学工程系，佛罗里达大学）；Stanford School of Medicine, Stanford（斯坦福医学院，斯坦福）；Functional Neuroimaging Unit, CRIUGM, Université de Montréal（功能神经影像单元，CRIUGM，蒙特利尔大学）；Centre de recherche du CHU Sainte-Justine, Université de Montréal（圣若泽中心研究，蒙特利尔大学）
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging（广东省医学影像与诊断技术工程实验室）；Information Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.（南方医科大学南华医院信息中心）；Division of Spine Surgery, Department of Orthopaedics, Nanfang hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.（南方医科大学南华医院骨科脊柱外科）
Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据洞察中心）；Sports Medicine Department, Sports Surgery Clinic（运动外科诊所运动医学部）；School of Health and Human Performance, Dublin City University（都柏林城市大学健康与人体运动学院）
In this paper we present an interactive tool that can be used to quantify fat infiltration in lumbar muscles, which is useful in studying fat infiltration and lower back pain (LBP) in adults. Currently, a qualitative assessment by visual grading via a 5-point scale is used to study fat infiltration in lumbar muscles from an axial view of lumbar-spine MR Images. However, a quantitative approach (on a continuous scale of 0-100\%) may provide a greater insight. In this paper, we propose a method to precisely quantify the fat deposition / infiltration in a user-defined region of the lumbar muscles…
Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. Thus, a model that learns the ambiguity and outputs a probability distribution of the target, would be valuable for medical applications to assess the uncertainty of diagnosis. In this paper, we propose a powerful generative model to learn a representation of ambiguity and to generate probabilistic outputs. Our model, named Coordinate Quantization Variational Autoencoder (CQ-VAE) employs a discrete latent space with an int…
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Klinikum rechts der Isar（Isar临床中心）；TUM School of Medicine and Health（慕尼黑工业大学医学与健康学院）；Institut fuer KI und Informatik in der Medizin（医学人工智能与信息学研究所）；German National Cohort（德国国家队列研究中心）
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Graz University of Technology（格拉茨工业大学）；University of Auckland（奥克兰大学）
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an uprigh…
Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection
University of Koblenz（科布伦茨大学）；Institute for Computational Visualistics（计算视觉研究所）；Institute for Medical Technology and Information Processing（医学技术与信息处理研究所）
The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
University of Utah School of Medicine（犹他大学医学院）；Thomas Jefferson University（托马斯·杰斐逊大学）；St. Michael’s Hospital（圣迈克尔医院）；Unity Health Toronto（多伦多联合医疗）；The Ohio State University（俄亥俄州立大学）；The Jackson Laboratory（杰克逊实验室）；Bunkerhilll Health（邦克希尔健康公司）；Universidade Federal de São Paulo（圣保罗联邦大学）；University of California San Francisco（加州大学旧金山分校）；Radiological Society of North America（北美放射学会）；Chiang Mai University Faculty of Medicine（清迈大学医学院）；Koc University School of Medicine（科奇大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Diagnósticos da América S.A.（美洲诊断公司）；Gold Coast University Hospital（黄金海岸大学医院）；Clinical Center University of Sarajevo（萨拉热窝大学临床中心）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）；Tallaght University Hospital（塔拉特大学医院）；Kingston Health Sciences Centre（金斯顿健康科学中心）；Queen’s University（女王大学）；Loyola University Medical Center（洛约拉大学医学中心）；George Washington University Hospital（乔治华盛顿大学医院）；Children’s National Hospital（国家儿童医院）；Scripps Clinical Medical Group（斯克里普斯临床医疗集团）
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Department of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital, Technical University of Munich, München, Germany（诊断与介入神经放射科，医学院，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Institut für KI und Informatik in der Medizin, TUM University Hospital, Technical University of Munich, München, Germany（医学人工智能与医学信息学研究所，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany（运动骨科部门，Rechts der Isar诊所，慕尼黑技术大学，慕尼黑，德国）；Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany（神经科，医学院，慕尼黑技术大学，慕尼黑，德国）；Department of Radiology, Weill Cornell Medicine, New York, USA（放射科，韦尔·科恩医学中心，纽约，美国）；Department of Computing, Imperial College London, London, UK（计算系，伦敦帝国理工学院，伦敦，英国）
University of Oxford（牛津大学）；Plexalis Ltd（Plexalis有限公司）
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical datas…
This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attributed to the use of transformer architectures. In this work, we argue that with a well-tuned three-stage pipeline comprising semantic segmentation, localization, and classification, convolutional networks outperform the state-of-the-art ap…
École de technologie supérieure（高等技术学院）；Fourth Military Medical University（第四军医大学）；Xidian University（西安电子科技大学）
Accurate localization and segmentation of intervertebral disc (IVD) is crucial for the assessment of spine disease diagnosis. Despite the technological advances in medical imaging, IVD localization and segmentation are still manually performed, which is time-consuming and prone to errors. If, in addition, multi-modal imaging is considered, the burden imposed on disease assessments increases substantially. In this paper, we propose an architecture for IVD localization and segmentation in multi-modal MRI, which extends the well-known UNet. Compared to single images, multi-modal data brings compl…
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
University of Regensburg（雷根斯堡大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；Northwestern University（西北大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Polytechnique Montreal（蒙特利尔综合理工大学）；Universite de Montreal（蒙特利尔大学）；Mila（米拉）；CRIUGM（蒙特利尔大学神经科学研究中心）
Labeling intervertebral discs is relevant as it notably enables clinicians to understand the relationship between a patient's symptoms (pain, paralysis) and the exact level of spinal cord injury. However manually labeling those discs is a tedious and user-biased task which would benefit from automated methods. While some automated methods already exist for MRI and CT-scan, they are either not publicly available, or fail to generalize across various imaging contrasts. In this paper we combine a Fully Convolutional Network (FCN) with inception modules to localize and label intervertebral discs. …
Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning
Faculty of Computer Science and Engineering（计算机科学与工程学院）；Ho Chi Minh City University of Technology（胡志明市技术大学）；Vietnam National University (HCMUT-VNU)（越南国家大学（HCMUT-VNU））；National Taiwan University（国立台湾大学）
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation
Medical Artificial Intelligence Lab, Westlake University（西交大学医学人工智能实验室）；Department of Information and Communication Engineering, School of Engineering, Institute of Science Tokyo（东京科学大学工程学院信息与通信工程系）；Department of Biomedical Engineering, The Chinese University of Hong Kong（香港中文大学生物医学工程系）
Brigham and Women’s Hospital（布列根和妇女医院）；Massachusetts General Hospital（麻省总医院）
The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this paper, we develop an efficient methodology to leverage the subject-matter-expertise stored in large-scale archival reporting and image data for a deep-learning approach to fully-automated lumbar spinal stenosis grading. Specifically, we introduce three major contributions: (1) a natural-language-processing scheme to extract level-by-level ground-truth labels from free-text radiology rep…
Background: Magnetic field inhomogeneities generate important geometric distortions in reconstructed echo-planar images. Various procedures were proposed for correcting these distortions on brain images; yet, few neuroimaging studies tailored and incorporated the use of these techniques in spinal cord diffusion MRI. Purpose: We present a comparative evaluation of distortion correction methods that use the reversed gradient polarity technique on spinal cord. We propose novel geometric metrics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spina…
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；University of Zurich（苏黎世大学）；Imperial College London（帝国理工学院）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University Medical Center Rostock（罗斯托克大学医学中心）；University of Freiburg（弗赖堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Augsburg University（奥格斯堡大学）
Indian Institute of Technology Madras (IITM)（印度理工学院马德拉斯分校）；Healthcare Technology Innovation Centre (HTIC), IITM（印度理工学院马德拉斯分校医疗技术创新中心）
Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations. However in surgery, image-guided spinal procedures continue to rely on CT and fluoroscopy, as MRI slice resolutions are typically insufficient. Building upon state-of-the-art single image super-resolution, we propose a reference-based, unpaired multi-contrast texture-transfer strategy for deep learning based in-plane and across-plane MRI super-resolution. We use the scattering transform to relate the te…
Sharif University of Technology（谢里夫理工大学）；Polytechnique Montreal（蒙特利尔理工学院）；Mila, Quebec AI Institute（米拉魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）
Labeling vertebral discs from MRI scans is important for the proper diagnosis of spinal related diseases, including multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy and cancer. Automatic labeling of the vertebral discs in MRI data is a difficult task because of the similarity between discs and bone area, the variability in the geometry of the spine and surrounding tissues across individuals, and the variability across scans (manufacturers, pulse sequence, image contrast, resolution and artefacts). In previous studies, vertebral disc labeling is often done aft…
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 …
Southwestern Hospital（西南医院）；Army Medical University（陆军军医大学）
Intervertebral discs (IVDs), as small joints lying between adjacent vertebrae, have played an important role in pressure buffering and tissue protection. The fully-automatic localization and segmentation of IVDs have been discussed in the literature for many years since they are crucial to spine disease diagnosis and provide quantitative parameters in the treatment. Traditionally hand-crafted features are derived based on image intensities and shape priors to localize and segment IVDs. With the advance of deep learning, various neural network models have gained great success in image analysis …
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
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）
Field strength-dependent performance variability in deep learning-based analysis of magnetic resonance imaging
Purdue University（普渡大学）；Indiana University School of Medicine（印第安纳大学医学院）；Medical Faculty and University Hospital Dresden, TUD Dresden University of Technology（德累斯顿工业大学德累斯顿医学院与大学医院）
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右伊萨尔医院）；University of Zurich（苏黎世大学）；Institut für KI und Informatik in der Medizin（医学人工智能与信息学研究所）；Imperial College London（伦敦帝国学院）
