Anatomy-Aware Text-Visual Fusion with Dual-Perspective Prompts for Fine-Grained Lumbar Spine Segmentation
College of Computer and Data Science（计算机与数据科学学院）；Shandong Technology and Business University（山东科技商务大学）；Engineering Research Center of Big Data Intelligence (Fuzhou University)（大数据智能工程研究中心（福州大学））；The Chinese University of Hong Kong（香港中文大学）；Case Western Reserve University（凯斯西储大学）
Sheng Lian, Jianlong Cai, Dengfeng Pan, Guang-Yong Chen, Hao Xu, Fan Zhang, Guodong Fan, Shuo Li
Accurate lumbar spine segmentation is crucial for diagnosing spinal disorders. Existing methods typically use coarse-grained segmentation strategies that lack the fine detail needed for precise diagnosis. Additionally, their reliance on visual-only models hinders the capture of anatomical semantics, leading to misclassified categories and poor segmentation details. To address these limitations, we present ATM-Net, an innovative framework that employs an anatomy-aware, text-guided, multi-modal fusion mechanism for fine-grained segmentation of lumbar substructures, i.e., vertebrae (VBs), intervertebral discs (IDs), and spinal canal (SC). ATM-Net adopts the Anatomy-aware Text Prompt Generator (ATPG) to adaptively convert image annotations into anatomy-aware prompts in different views. These insights are further integrated with image features via the Holistic Anatomy-aware Semantic Fusion (HASF) module, building a comprehensive anatomical context. The Channel-wise Contrastive Anatomy-Aware Enhancement (CCAE) module further enhances class discrimination and refines segmentation through class-wise channel-level multi-modal contrastive learning. Extensive experiments on the MRSpineSeg and SPIDER datasets demonstrate that ATM-Net significantly outperforms state-of-the-art methods, with consistent improvements regarding class discrimination and segmentation details. For example, ATM-Net achieves Dice of 79.39% and HD95 of 9.91 pixels on SPIDER, outperforming the competitive SpineParseNet by 8.31% and 4.14 pixels, respectively.
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…
Quantitative computed tomography (QCT) is a standard method to determine bone mineral density (BMD) in the spine. Traditionally single 8 - 10 mm thick slices have been analyzed only. Current spiral CT scanners provide true 3D acquisition schemes allowing for a more differential BMD analysis and an assessment of geometric parameters, which may improve fracture prediction. We developed a novel 3D segmentation approach that combines deformable balloons, multi seeded volume growing, and dedicated morphological operations to extract the vertebral bodies. An anatomy-oriented coordinate system attach…
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）
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）
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…
The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU) was up to 3 percent better for the best performing instance segmentation model, the average pixel accuracy and weighted IoU were slightly better for the best performing semantic segmentation model.…
Center for Data Science, Peking University（北京大学数据科学中心）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用国家工程实验室）；National Biomedical Imaging Center, Peking University（北京大学生物医学成像国家中心）
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis. However, existing deep learning models with millions of parameters often fail to learn with only hundreds or dozens of medical images. These models also ignore the contextual relationship between adjacent objects, such as between vertebras and intervertebral discs. This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. Specifically, in learning, we divide the localization objective into two …
Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet
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（印度统计研究所）
本研究提出基于改进注意力 U-Net 架构和新型掩码逻辑的方法，用于 3D 腰椎 MRI 数据的椎骨全景分割与标注，实现 99.5% 的高准确率，显著推进该领域最先进技术。
Klinikum rechts der Isar（伊萨尔右岸医院）；Technische Universität München（慕尼黑工业大学）
Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep ne…
Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications
Center for Informatics Science, School of Information Technology and Computer Science, Nile University（信息科学中心，信息科技与计算机科学学院，尼罗大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，兵库大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医学工程研究所，兵库大学）
In this paper we present a new 3D segmentation approach for the vertebrae of the lower thoracic and the lumbar spine in spiral computed tomography datasets. We implemented a multi-step procedure. Its main components are deformable models, volume growing, and morphological operations. The performance analysis that included an evaluation of accuracy using the European Spine Phantom, and of intra-operator precision using clinical CT datasets from 10 patients highlight the potential for clinical use. The intra-operator precision of the segmentation procedure was better than 1% for Bone Mineral Den…
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）
Registration plays an important role in medical image analysis. Deep learning-based methods have been studied for medical image registration, which leverage convolutional neural networks (CNNs) for efficiently regressing a dense deformation field from a pair of images. However, CNNs are limited in its ability to extract semantically meaningful intra- and inter-image spatial correspondences, which are of importance for accurate image registration. This study proposes a novel end-to-end deep learning-based framework for unsupervised affine and diffeomorphic deformable registration, referred as A…
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…
Hybrid Convolution Neural Network Integrated with Pseudo-Newton Boosting for Lumbar Spine Degeneration Detection
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工大学计算机科学与工程学院）；Department of Information Science and Technology, College of Engineering Guindy, Anna University（安娜大学金迪工程学院信息科学与技术系）；Integrated Management Coastal Zones Research Institute, Universitat Politecnica de Valencia（瓦伦西亚理工大学海岸带综合管理研究所）
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）
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…
University of Maryland（马里兰大学）；PAII Inc.（PAII公司）；Alibaba Group DAMO Academy（阿里巴巴集团达摩院）；Xpeng Motors（小鹏汽车）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for ac…
Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss
School of Biomedical Engineering & Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学生物医学工程学院与Med-X研究院）；Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education（教育部数字医学临床转化工程研究中心）；Department of Spine Surgery, Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院脊柱外科）；TAOiMAGE Medical Technologies Corporation（TAOiMAGE医疗科技有限公司）；School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）
Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO)（瓦伦西亚卫生与生物医学研究促进基金会（FISABIO））；Hospital Arnau de Vilanova（阿尔瑙·德·比拉诺瓦医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Regional ministry of Universal Health and Public Health in Valencia（瓦伦西亚全民健康与公共卫生部）
One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different str…
Technical University of Munich（慕尼黑工业大学）；Friedrich Miescher Institute for Biomedical Engineering（弗里德里希·米舍尔生物医学工程研究所）
Purpose: To use and test a labelling algorithm that operates on two-dimensional (2D) reformations, rather than three-dimensional (3D) data to locate and identify vertebrae. Methods: We improved the Btrfly Net (described by Sekuboyina et al) that works on sagittal and coronal maximum intensity projections (MIP) and augmented it with two additional components: spine-localization and adversarial a priori-learning. Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net. We investigated the superiority of the pro…
VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences
TUM University Hospital（慕尼黑技术大学医院）；Technical University of Munich (TUM)（慕尼黑技术大学）；Institute of Biomedical Engineering（生物医学工程学院）；Polytechnique Montreal（蒙特利尔理工学院）；Mila -- Quebec AI Institute（魁北克人工智能研究所）；University of Zurich（苏黎世大学）；Rostock University Medical Center（罗斯托克大学医学中心）；University Hospital Heidelberg（海德堡大学医院）；Medical Center -- University of Freiburg（弗赖堡大学医学中心）；Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC)（马克斯-德尔布吕克分子医学中心（马克斯·普朗克协会））；University Hospital Augsburg（奥格斯堡大学医院）
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…
Korea Institute of Science and Technology（韩国科学技术研究院）；Korea University of Science and Technology（韩国科学技术大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we develo…
Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification
Department of Computer Science and Engineering, BRAC University（布拉格大学计算机科学与工程系）；Department of Electrical and Electronic Engineering, University of Dhaka（达卡大学电子与电气工程系）；Department of Electrical Engineering, Qatar University（卡塔尔大学电子工程系）；Department of Radiology, Hamad Medical Corporation（哈马德医疗中心放射科）
National Institutes of Health（美国国立卫生研究院）；University of California-Irvine（加利福尼亚大学尔湾分校）
Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thor…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；University of Wisconsin, Madison（威斯康星大学麦迪逊分校）
The vertebral levels of the spine provide a useful coordinate system when making measurements of plaque, muscle, fat, and bone mineral density. Correctly classifying vertebral levels with high accuracy is challenging due to the similar appearance of each vertebra, the curvature of the spine, and the possibility of anomalies such as fractured vertebrae, implants, lumbarization of the sacrum, and sacralization of L5. The goal of this work is to develop a system that can accurately and robustly identify the L1 level in large heterogeneous datasets. The first approach we study is using a 3D U-Net …
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Department of Radiology, Northwestern University, Chicago, IL, USA（西北大学放射科）；Data Science Institute, Vanderbilt University, Nashville, TN, USA（范德比尔特大学数据科学研究院）；Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二医院骨科）；Second Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二临床医学院）；School of Computer Science, University of Sydney, Sydney, NSW, Australia（悉尼大学计算机科学学院）
SurgPointTransformer: Vertebrae Shape Completion with RGB-D Data
Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zürich（苏黎世大学、巴尔格里斯特大学医院骨科计算机科学研究所）；Balgrist University Hospital, University of Zurich（苏黎世大学巴尔格里斯特大学医院）
SpinalSAM-R1: A Vision-Language Multimodal Interactive System for Spine CT Segmentation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；Department of Orthopedics, Qilu Hospital, Shandong University（骨科部，齐鲁医院，山东大学）；Key Laboratory of Qingdao in Medicine and Engineering（医学与工程青岛重点实验室）
SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Northeast Petroleum University（东北石油大学）；Northeastern University（东北大学）；Southern Medical University（南方医科大学）
Technical University of Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Helmholtz Zentrum München（亥姆霍兹慕尼黑中心）；Graz University of Technology（格拉茨工业大学）；Medical University of Graz（格拉茨医科大学）；The University of Auckland（奥克兰大学）；iFLYTEK Research（科大讯飞研究院）
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerS…
