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18 results for “Lumbar segmentation”
SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark
<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.    Modic changes (type I, II or III)</p> <p>2.    Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.    Spondylolisthesis (binary)</p> <p>4.    Disc herniation (binary)</p> <p>5.    Disc narrowing (binary)</p> <p>6.    Disc bulging (binary)</p> <p>7.    Pfirrman grade (grade 1 to 5). </p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>
LumASe: A Lumbar Vertebra Anatomical Region Segmentation Dataset
<p>Lumbar vertebra anatomical region segmentation is crucial in an automated spine processing pipeline. To boost the research in automated lumbar vertebra anatomical region segmentation, we propose a new dataset called LumASe. The entire dataset consists of 663 vertebrae ranging from L1 to L5 which are cropped from lumbar spine CT scans. The data was acquired at ShengJing Hospital of China Medical University using three major manufacturers (Philips, Siemens and Toshiba). Cases with vertebral fractures, metallic implants, bone tumors and foreign materials are excluded. All 3D CT lumbar spine images have corresponding segmentation masks annotated at the voxel level by 3 physicians using the Pair annotation package. In each vertebra, we consider seven anatomical regions as the region of interest including superior articular process (SAP), vertebral body (VB), transverse process (TP), lamina (L) pedicle (P), spinous process (SP) and inferior articular process (IAP).</p>
LumVBCanSeg: A Lumbar Vertebral Body Cancellous Bone Segmentation Dataset
<p>To address the lumbar vertebral body cancellous bone segmentation problem, we propose a new dataset called LumVBCanSeg. The entire dataset consists of 185 lumbar CT scans acquired using multiple CT scanners, including the two manufacturers(Philips, Siemens). All data were obtained at ShengJing Hospital of China Medical University. Cases involving vertebral fractures, metal implants, bone tumors and foreign materials were omitted. All data were resampled to an isotropic resolution of 1mm × 1mm × 1mm. Each lumbar 3D CT image has a corresponding segmentation mask annotated at voxel level using the medical image annotation package Pair. Annotations cover five lumbar vertebral body cancellous bones from L1 to L5, with labels ranging from 1 to 5. All annotations were performed by 3 physicians followed by further refinement, dismissal or approval by one physician with more than 30 years of experience in lumbar imaging. </p>
Normalized CT images and reference segmentations of thoracic and lumbar vertebrae from the CSI 2014 workshop
<p>This is the dataset of the vertebra segmentation challenge of the <a href="http://csi-workshop.weebly.com/challenges.html">CSI 2014 workshop</a> that was held in conjunction with MICCAI 2014.</p> <ul> <li><strong>1-10</strong>: Training set, scans of 10 young adult (16-35 years old)</li> <li><strong>11-15</strong>: Test set, scans of 5 young adult (20-35 years old)</li> <li><strong>16-20</strong>: Test set, scans of 5 patients with vertebral compression fractures </li> </ul> <p>Scans were acquired at the Department of Radiological Sciences, University of California, Irvine, School of Medicine and were published under the <a href="http://opendatacommons.org/licenses/pddl/1.0/">ODC Public Domain Dedication and License</a> on <a href="http://spineweb.digitalimaginggroup.ca/">SpineWeb</a> (datasets 2 and 15). The dataset and challenge is further described in this publication: <a href="http://dx.doi.org/10.1016/j.compmedimag.2015.12.006">A multi-center milestone study of clinical vertebral CT segmentation</a></p> <p>The data that is published here has been normalized:</p> <ul> <li>Voxel values are Hounsfield values and have been clipped to [-1000, 3095]</li> <li>Image origin has been set to 0,0,0</li> <li>Image orientation has been standardized to RAI orientation</li> <li>Small islands and other obvious mistakes have been removed</li> <li>Segmentation masks have been smoothed with a 2x2x2 median filter</li> <li>Vertebrae have been anatomically labeled (8 = T1, 9 = T2, ..., 24 = L5)</li> <li>Because not always all visible vertebrae were segmented in the original data, only segmentations of the thoracic and lumbar vertebrae have been retained</li> </ul> <p><strong>License</strong></p> <p>This dataset is released under the <a href="https://opendatacommons.org/licenses/by/1-0/">Open Data Commons Attribution License</a> (which the original license allows me to do). When using this dataset for publication of any kind, please reference the following paper to meet the attribution requirement:</p> <blockquote> <p>Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, Hammernik K, Urschler M, Ibragimov B, Korez R, Vrtovec T, Castro-Mateos I, Pozo JM, Frangi AF, Summers RM, Li S. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph. 2016; 49:16-28. doi: 10.1016/j.compmedimag.2015.12.006.</p> </blockquote> <p>There is no need to reference this upload, referencing the original authors is sufficient.</p> <p><strong>Notes</strong></p> <p>The dataset contains 2 cases with only 4 lumbar vertebrae in which L5/S1 has not been segmented (i.e., label 25 is missing).</p> <p>The resolution and segmentation quality of the diseased cases (16-20) is quite low.</p>
Lumbar Segmental Stabilization and TENS in Lumbar Disc Herniation
ClinicalTrials.gov study NCT01640431. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Segmental Mobilization vs Entire Spine Mobilization In Lumbar Spondylosis
ClinicalTrials.gov study NCT04158115. IPD Sharing: NO. Countries: 1. Publications: 2.
Preventive Effect of Limited Decompression on Adjacent Segment Following Posterior Lumbar Interbody Fusion
ClinicalTrials.gov study NCT04469387. IPD Sharing: YES. Countries: 1. Publications: 7.
Effect of Pre-existing Adjacent Segment Degeneration on Long-term Effectiveness After Lumbar Fusion Surgery
ClinicalTrials.gov study NCT04467944. IPD Sharing: YES. Countries: 1. Publications: 6.
Effect of Open Chain Versus Closed Chain Segmental Control Exercises on CSA of Lumbar Multifidus Muscle in Chronic MLBP
ClinicalTrials.gov study NCT06009263. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Study of Imaging and Endoplant-related Mechanical Complications After Long Segment Fixation and Fusion of Degenerative Lumbar Scoliosis (DLS)
ClinicalTrials.gov study NCT04960423. IPD Sharing: NO. Countries: 1. Publications: 0.
3rd-year Post-surgical Evaluation of Adjacent Segment Disc Degeneration Onset in Lumbar Spine (Spinal Fusion vs. Lumbar Arthroplasty With Disc Replacement)
ClinicalTrials.gov study NCT05561322. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Validity and Predictive Value of Manual Clinical Test to Identify Symptomatic Segments in Lumbar Chronic Patients
ClinicalTrials.gov study NCT02777450. IPD Sharing: NO. Countries: 1. Publications: 0.
Exploring the Distribution Patterns and Infrared Characteristics of Force-sensitive Acupoints in Different Lumbar Nerve Segments in Patients With LDH Based on "Press Quickly" Theory
ClinicalTrials.gov study NCT06318156. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of the Relation Between the Fat Infiltration of the Multifidus Muscle and the Lumbar Foraminal Stenosis, by 2D and 3D Segmentation
ClinicalTrials.gov study NCT06018155. IPD Sharing: NO. Countries: 1. Publications: 0.
Restoring Segmental Lumbar Lordosis After Failed Previous Fusion at the Same Level
ClinicalTrials.gov study NCT07050290. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Segmental Stabilization on the Anticipatory Postural Adjustment of Subjects With Lumbar Pain
ClinicalTrials.gov study NCT02815358. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of the Posterior Ligamentous Complex on the Adjacent Segments Degeneration After Lumbar Surgery
ClinicalTrials.gov study NCT04946487. IPD Sharing: NO. Countries: 0. Publications: 0.
Evaluation of the Effect of Revision Surgery of Lumbar Adjacent Segment Degeneration
ClinicalTrials.gov study NCT04970862. IPD Sharing: NO. Countries: 0. Publications: 0.
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