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 dataset of whole spine scans and matches or exceeds the performance of previous systems on lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能信息处理重点实验室）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）；Beijing Jishuitan Hospital（北京积水潭医院）；School of Medicine, South China University of Technology（华南理工大学医学院）
University of California, Los Angeles（加州大学洛杉矶分校）；Tencent Hippocrates Research Lab（腾讯希波克拉底研究实验室）；The University of Hong Kong（香港大学）；VoxelCloud, Inc.（VoxelCloud公司）
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spine radiographs are the most cost-effective and accessible modality for imaging the spine. Reliable and accurate vertebrae segmentation in spine radiographs is crucial in image-guided spinal assessment, disease diagnosis, and treatment planning. Conventional assessments rely on tedious and time-consuming manual measurement, which is subject to inter-observer variability. A fully automatic method that can accurately id…
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…
Helmholtz Zentrum Muenchen（慕尼黑亥姆霍兹中心）；Technische Universitaet Muenchen（慕尼黑工业大学）；Reply SpA（瑞普公司）；Johns Hopkins University（约翰斯·霍普金斯大学）
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual he…
We propose a new, two-stage approach to the vertebrae centroid detection and localization problem. The first stage detects where the vertebrae appear in the scan using 3D samples, the second identifies the specific vertebrae within that region-of-interest using 2D slices. Our solution utilizes new techniques to improve the accuracy of the algorithm such as a revised approach to dense labelling from sparse centroid annotations and usage of large anisotropic kernels in the base level of a U-net architecture to maximize the receptive field. Our method improves the state-of-the-art's mean localiza…
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）
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…
Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications. While deep learning strategies have brought to this field significant improvements over recent years, transitional and pathological vertebrae are still plaguing most existing approaches as a consequence of their poor representation in training datasets. Alternatively, proposed non-learning based methods take benefit of prior knowledge to handle such particular cases. In this work we propose to combine both strategies. To this purpose we introduce an iterative cycle in which individual…
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.…
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）
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）
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…
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…
