Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection

• URL: http://arxiv.org/abs/2412.05065v1
• Date: Fri, 06 Dec 2024 14:23:42 GMT
• Title: Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection
• Authors: Lara Blomenkamp, Ivanna Kramer, Sabine Bauer, Kevin Weirauch, Dietrich Paulus,
• Abstract summary: We present a novel method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies. Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds.
• Score: 0.4194295877935868
• License:
• Abstract: Patient-specific 3D spine models serve as a foundation for spinal treatment and surgery planning as well as analysis of loading conditions in biomechanical and biomedical research. Despite advancements in imaging technologies, the reconstruction of complete 3D spine models often faces challenges due to limitations in imaging modalities such as planar X-Ray and missing certain spinal structures, such as the spinal or transverse processes, in volumetric medical images and resulting segmentations. In this study, we present a novel accurate and time-efficient method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies obtained from segmented magnetic resonance images (MRI). In our method, we use an affine transformation to align artificial vertebra models with patient-specific incomplete vertebrae. The transformation matrix is derived from vertebra landmarks, which are automatically detected on the vertebra endplates. The results of our evaluation demonstrate the high accuracy of the performed registration, achieving an average point-to-model distance of 1.95 mm. Additionally, in assessing the morphological properties of the vertebrae and intervertebral characteristics, our method demonstrated a mean absolute error (MAE) of 3.4{\deg} in the angles of functional spine units (FSUs), emphasizing its effectiveness in maintaining important spinal features throughout the transformation process of individual vertebrae. Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds, showcasing its time-efficiency. Clinical relevance: the fast and accurate reconstruction of spinal models from incomplete input data such as segmentations provides a foundation for many applications in spine diagnostics, treatment planning, and the development of spinal healthcare solutions.

Related papers

• Spinal ligaments detection on vertebrae meshes using registration and 3D edge detection [0.4194295877935868]
  The proposed method is able to detect 66 spinal ligament attachment points by using a step-wise approach. The landmark detection requires approximately 3.0 seconds per vertebra, providing a substantial improvement over existing methods.
  arXiv  Detail & Related papers  (2024-12-06T14:39:06Z)
• SurgPointTransformer: Vertebrae Shape Completion with RGB-D Data [0.0]
  This study introduces an alternative, radiation-free approach for reconstructing the 3D spine anatomy using RGB-D data. We introduce SurgPointTransformer, a shape completion approach for surgical applications that can accurately reconstruct the unexposed spine regions from sparse observations of the exposed surface. Our method significantly outperforms the state-of-the-art baselines, achieving an average Chamfer Distance of 5.39, an F-Score of 0.85, an Earth Mover's Distance of 0.011, and a Signal-to-Noise Ratio of 22.90 dB.
  arXiv  Detail & Related papers  (2024-10-02T11:53:28Z)
• SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors [10.431439196002842]
  We introduce a residual visual Mamba layer to model the deep semantic features and long-range spatial dependencies of 3D spinal data. We also propose a novel spinal shape prior module that captures specific anatomical information of the spine from medical images. SpineMamba achieves superior segmentation performance, exceeding it by up to 2 percentage points.
  arXiv  Detail & Related papers  (2024-08-28T15:59:40Z)
• 3D Vertebrae Measurements: Assessing Vertebral Dimensions in Human Spine Mesh Models Using Local Anatomical Vertebral Axes [0.4499833362998489]
  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.
  arXiv  Detail & Related papers  (2024-02-02T14:52:41Z)
• 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)
• Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
  This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
  arXiv  Detail & Related papers  (2022-06-27T10:31:03Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• 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)
• Analysis of Scoliosis From Spinal X-Ray Images [17.8260780895433]
  Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Scoliosis is a congenital disease in which the spine is deformed from its normal shape. We propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
  arXiv  Detail & Related papers  (2020-04-15T05:36:28Z)

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.