Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation

• URL: http://arxiv.org/abs/2001.02001v1
• Date: Tue, 7 Jan 2020 12:34:42 GMT
• Title: Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
• Authors: Firat Ozdemir, Christine Tanner, Orcun Goksel
• Abstract summary: Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics. We propose a method to encode the physics of ultrasound propagation into a factor graph formulation for the purpose of bone surface delineation.
• Score: 4.669073579457748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation. We herein propose a method to encode the physics of ultrasound propagation into a factor graph formulation for the purpose of bone surface delineation. In this graph structure, unary node potentials encode the local likelihood for being a soft tissue or acoustic-shadow (behind bone surface) region, both learned through image descriptors. Pair-wise edge potentials encode ultrasound propagation constraints of bone surfaces given their large acoustic-impedance difference. We evaluate the proposed method in comparison with four earlier approaches, on in-vivo ultrasound images collected from dorsal and volar views of the forearm. The proposed method achieves an average root-mean-square error and symmetric Hausdorff distance of 0.28mm and 1.78mm, respectively. It detects 99.9% of the annotated bone surfaces with a mean scanline error (distance to annotations) of 0.39mm.

Related papers

• Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging [39.597735935731386]
  A class-aware cartilage bone segmentation network with geometry-constraint post-processing is presented to capture patient-specific rib skeletons. A dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients. Results demonstrate that the proposed graph-based registration method can robustly and precisely map the path from CT template to individual patients.
  arXiv  Detail & Related papers  (2024-06-06T14:15:15Z)
• Anatomical Region Recognition and Real-time Bone Tracking Methods by Dynamically Decoding A-Mode Ultrasound Signals [4.595636071139437]
  This paper introduces a deep learning-based method for anatomical region recognition and bone tracking using A-mode ultrasound signals. The algorithm is capable of simultaneously performing bone tracking and identifying the anatomical region where the A-mode ultrasound transducer is placed. The experiment showed a 97% accuracy in the classification of the anatomical regions and a precision of around 0.5$pm$1mm under dynamic tracking conditions.
  arXiv  Detail & Related papers  (2024-05-29T22:04:40Z)
• Deep Learning based acoustic measurement approach for robotic applications on orthopedics [4.399658501226972]
  We propose a novel deep learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US) In this study, we proposed a novel deep learning structure to improve the accuracy of bone tracking by an A-mode ultrasound (US)
  arXiv  Detail & Related papers  (2024-03-09T11:09:45Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph [49.11220791279602]
  It is challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications. A graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup.
  arXiv  Detail & Related papers  (2023-07-07T18:57:21Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging [4.287216236596808]
  We propose to use a patch-like wearable ultrasound solution to capture the reflective bone surfaces from multiple imaging angles. Our wearable ultrasound system can potentially provide intuitive and accurate interventional guidance for clinicians in augmented reality setting.
  arXiv  Detail & Related papers  (2022-11-11T02:15:48Z)
• Orientation-guided Graph Convolutional Network for Bone Surface Segmentation [51.51690515362261]
  We propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. Our approach improves over the state-of-the-art methods by 5.01% in connectivity metric.
  arXiv  Detail & Related papers  (2022-06-16T23:01:29Z)
• 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)
• XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors [118.27130593216096]
  A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
  arXiv  Detail & Related papers  (2020-12-04T05:08:53Z)

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.