3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts

• URL: http://arxiv.org/abs/2304.14510v1
• Date: Tue, 18 Apr 2023 21:46:42 GMT
• Title: 3D Patient-specific Modelling and Characterisation of Muscle-Skeletal Districts
• Authors: Martina Paccini, Giuseppe Patan\`e, Michela Spagnuolo
• Abstract summary: We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models. For the qualitative and quantitative validation, we will discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work addresses the patient-specific characterisation of the morphology and pathologies of muscle-skeletal districts (e.g., wrist, spine) to support diagnostic activities and follow-up exams through the integration of morphological and tissue information. We propose different methods for the integration of morphological information, retrieved from the geometrical analysis of 3D surface models, with tissue information extracted from volume images. For the qualitative and quantitative validation, we will discuss the localisation of bone erosion sites on the wrists to monitor rheumatic diseases and the characterisation of the three functional regions of the spinal vertebrae to study the presence of osteoporotic fractures. The proposed approach supports the quantitative and visual evaluation of possible damages, surgery planning, and early diagnosis or follow-up studies. Finally, our analysis is general enough to be applied to different districts.

Related papers

• Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection [0.4194295877935868]
  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.
  arXiv  Detail & Related papers  (2024-12-06T14:23:42Z)
• Anatomy-guided Pathology Segmentation [56.883822515800205]
  We develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods.
  arXiv  Detail & Related papers  (2024-07-08T11:44:15Z)
• Recognizing Identities From Human Skeletons: A Survey on 3D Skeleton Based Person Re-Identification [60.939250172443586]
  Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. We provide a comprehensive review and analysis of recent SRID advances. A thorough evaluation of state-of-the-art SRID methods is conducted over various types of benchmarks and protocols to compare their effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-01-27T04:52:24Z)
• 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)
• 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)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM [0.0]
  We use a modified HighRes3DNet model for solving brain MRI volumetric landmark detection problem. Grad-CAM produces a coarse localization map highlighting the regions the model is focusing.
  arXiv  Detail & Related papers  (2021-11-01T11:14:23Z)
• 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)
• Gait analysis with curvature maps: A simulation study [3.867363075280544]
  We propose to focus our attention on extracting relevant curvature information from a body surface provided by a depth camera. This research set the grounds for the future development of a curvature-based gait analysis system for healthcare professionals.
  arXiv  Detail & Related papers  (2021-06-22T00:59:17Z)
• 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)

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.