3D Vertebrae Measurements: Assessing Vertebral Dimensions in Human Spine Mesh Models Using Local Anatomical Vertebral Axes

• URL: http://arxiv.org/abs/2402.01462v1
• Date: Fri, 2 Feb 2024 14:52:41 GMT
• Title: 3D Vertebrae Measurements: Assessing Vertebral Dimensions in Human Spine Mesh Models Using Local Anatomical Vertebral Axes
• Authors: Ivanna Kramer, Vinzent Rittel, Lara Blomenkamp, Sabine Bauer, Dietrich Paulus
• Abstract summary: We introduce a novel, fully automated method for measuring vertebral morphology using 3D meshes of lumbar and thoracic spine models. Our experimental results demonstrate the method's capability to accurately measure low-resolution patient-specific vertebral meshes with mean absolute error (MAE) of 1.09 mm. Our qualitative analysis indicates that measurements obtained using our method on 3D spine models can be accurately reprojected back onto the original medical images if these images are available.
• Score: 0.4499833362998489
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vertebral morphological measurements are important across various disciplines, including spinal biomechanics and clinical applications, pre- and post-operatively. These measurements also play a crucial role in anthropological longitudinal studies, where spinal metrics are repeatedly documented over extended periods. Traditionally, such measurements have been manually conducted, a process that is time-consuming. In this study, we introduce a novel, fully automated method for measuring vertebral morphology using 3D meshes of lumbar and thoracic spine models.Our experimental results demonstrate the method's capability to accurately measure low-resolution patient-specific vertebral meshes with mean absolute error (MAE) of 1.09 mm and those derived from artificially created lumbar spines, where the average MAE value was 0.7 mm. Our qualitative analysis indicates that measurements obtained using our method on 3D spine models can be accurately reprojected back onto the original medical images if these images are available.

Related papers

• SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation [0.0]
  This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies. We achieved outstanding results on two publicly available spine X-Ray datasets, with successful identification of 97.8% and 99.6% of annotated vertebrae.
  arXiv  Detail & Related papers  (2024-11-01T02:51:21Z)
• Registration of Longitudinal Spine CTs for Monitoring Lesion Growth [32.449176315776114]
  We present a novel method to automatically align longitudinal spine CTs. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations.
  arXiv  Detail & Related papers  (2024-02-14T17:43:50Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples [6.381153836752796]
  We present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA) for processing 3D tissue images. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias.
  arXiv  Detail & Related papers  (2023-07-27T14:48:02Z)
• BOSS: Bones, Organs and Skin Shape Model [10.50175010474078]
  We propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA, our approach offers a holistic representation of the body.
  arXiv  Detail & Related papers  (2023-03-08T22:31:24Z)
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models [87.48867245544106]
  Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) Existing solutions based on machine learning typically train a model to directly map measurements to medical images. We propose a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models.
  arXiv  Detail & Related papers  (2021-11-15T05:41:12Z)
• Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition [0.6176955945418618]
  Powerful tools of artificial intelligence such as deep learning are making it feasible now to segment the entire 3D image and generate accurate measurements of all internal anatomy. These will enable the overcoming of the severe bottleneck that existed previously, namely, the need for manual segmentation. These measurements were hitherto unavailable thereby limiting the field to a very small and limited subset.
  arXiv  Detail & Related papers  (2021-06-01T17:30:45Z)
• Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data [68.8204255655161]
  Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane. We present a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression. For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations.
  arXiv  Detail & Related papers  (2020-07-20T12:45:38Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  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. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)
• Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
  In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
  arXiv  Detail & Related papers  (2020-06-18T09:49:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.