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. Moreover, the inferences of the instance segmentation models are easier to implement for further processing pipelines in clinical decision support.
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…
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…
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。
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）
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）
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）
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…
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）
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 …
This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes. SpineNetV2 improves over the original SpineNet software in two ways: (1) The vertebral body detection stage is significantly faster, more accurate and works across a range of fields-of-view (as opposed to just lumbar scans). (2) Radiological grading adopts a more p…
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）
Huazhong University of Science and Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology（香港科技大学）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for…
Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical image analysis systems have even been FDA-cleared. Despite the progress, challenges remain to build DNNs as reliable as human expert doctors. It is known that DNN classifiers may not be robust to noises: by adding a small amount of noise to an input image, a DNN classifier may make a wrong classification of the noisy image (i.e., in-distribution adversarial sam…
University of Science and Technology of China（中国科学技术大学）；Guangzhou First People’s Hospital（广州市第一人民医院）；South China University of Technology（华南理工大学）
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our model building has to address the following challenges. Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset. Thus, we resort to learn from a union of multiple datasets, with each dataset containing the images that are partially labeled. Secondly, along the line of partial labelling, we contribute an open-source, large-scale verteb…
Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance i…
ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
Pohang Stroke and Spine Hospital（浦项中风与脊柱医院）；Handong Global University（韩东全球大学）；Pohang University of Science and Technology（浦项科技大学）；Kyungpook National University Hospital（庆北国立大学医院）
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…
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）
Technical University of Munich（慕尼黑工业大学）；University Medical Center Freiburg（弗莱堡大学医学中心）
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iterat…
