In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.
University of Marburg（马尔堡大学）；TU Graz（格拉茨技术大学）；BioTechMed（生物技术医学）
Due to the aging population, spinal diseases get more and more common nowadays; e.g., lifetime risk of osteoporotic fracture is 40% for white women and 13% for white men in the United States. Thus the numbers of surgical spinal procedures are also increasing with the aging population and precise diagnosis plays a vital role in reducing complication and recurrence of symptoms. Spinal imaging of vertebral column is a tedious process subjected to interpretation errors. In this contribution, we aim to reduce time and error for vertebral interpretation by applying and studying the GrowCut-algorithm…
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…
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…
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）
Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning
School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）；Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所生物工程与计算实验室）；Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Alma Mater Studiorum – Università di Bologna（博洛尼亚大学医学与外科科学系（DIMEC））；Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所诊断与介入放射学）；Department of Biomedical and Neuromotor Sciences, University of Bologna（博洛尼亚大学生物医学与神经运动科学系）；nd Orthopedics and Trauma Unit, IRCCS Istituto Ortopedico Rizzoli（里扎利骨科研究所第二骨科与创伤单元）；Independent Researcher（独立研究员）
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…
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）
AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis
Biomechanics and Spine Research Group, School of Mechanical, Medical, and Process Engineering, Faculty of Engineering, Queensland University of Technology（生物力学与脊柱研究组，机械、医学与过程工程学院，工程学院，昆士兰理工大学）；Centre for Biomedical Technologies, School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Queensland University of Technology（生物医学技术中心，机械、医学与过程工程学院，工程学院，昆士兰理工大学）；Orthopaedics Department, Queensland Children’s Hospital（骨科部门，昆士兰儿童医院）
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）
Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)（圣保罗大学数学与计算机科学学院（ICMC））；Ribeirão Preto Medical School (FMRP), University of São Paulo (USP)（圣保罗大学里贝朗普雷图医学院（FMRP））
Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Gr…
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与先进计算重点实验室）
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…
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal（蒙特利尔理工大学生物医学工程研究所神经多实验室）；AIMI, Stanford University（斯坦福大学AIMI）；Mila, Quebec AI Institute（魁北克人工智能研究所Mila）；Polytechnique Montreal（蒙特利尔理工大学）
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dat…
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）
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。
