We present a method to address the challenging problem of segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN and a segmentation FCN. More specifically, in the first step we train a regression 3D FCN (we call it "LocalizationNet") to find the bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it "SegmentationNet") is then developed, which after training, can perform a pixel-wise multi-class segmentation to map a cropped lumber region volumetric data to its volume-wise labels. Evaluated on publicly available datasets, our method achieved an average Dice coefficient of 95.77 $\pm$ 0.81% and an average symmetric surface distance of 0.37 $\pm$ 0.06 mm.
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…
Novartis Pharmaceutical Company（诺华制药公司）；Big Data Institute, University of Oxford（牛津大学大数据研究所）
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXN…
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。
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…
In this paper we introduce the Ladder Algorithm; a novel recurrent algorithm to detect repetitive structures in natural images with high accuracy using little training data. We then demonstrate the algorithm on the task of extracting vertebrae from whole spine magnetic resonance scans with only lumbar MR scans for training data. It is shown to achieve high perforamance with 99.8% precision and recall, exceeding current state of the art approaches for lumbar vertebrae detection in T1 and T2 weighted scans. It also generalises without retraining to whole spine images with minimal drop in accurac…
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）
In this paper a new technique is presented that extracts the geometry of lumbar vertebral bodies from spiral CT scans. Our new multi-step segmentation approach yields highly accurate and precise measurement of the bone mineral density (BMD) in different volumes of interest which are defined relative to a local anatomical coordinate systems. The approach also enables the analysis of the geometry of the relevant vertebrae. Intra- and inter operator precision for segmentation, BMD measurement and position of the coordinate system are below 1.5% in patient data, accuracy errors are below 1.5% for …
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…
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）
National Institutes of Health（美国国立卫生研究院）；University of California, Irvine School of Medicine（加州大学欧文分校医学院）
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies. Segmentation is especially challenging in the presence of pathology such as vertebral compression fractures. In this paper, we propose a method to produce segmentations for osteoporotic compression fractured vertebrae by applying a multi-atlas joint label fusion technique for clinical CT images. A total of 170 thoracic and lumbar vertebrae were evaluated using atlases from five patients with varying degrees of sp…
HwaMei Hospital, University of Chinese Academy of Sciences (UCAS)（中国科学院大学华美医院）；Ningbo Institute of Life and Health Industry, UCAS（中国科学院大学宁波生命与健康产业研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis. Computed tomography (CT) is a common clinical examination to screen for this disease at early stages. However, the faint radiological appearances and unspecific symptoms lead to a high risk of missed diagnosis, especially for the mild vertebral fractures. In this paper, we argue that reinforcing the faint fracture features to encourage the inter-class separability is the key to improving the accuracy. Motivated by this, we propose a supervised contrastive learning …
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）
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…
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…
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）
