Skip to content
  • Our Work
    • Fields
      • Cardiology
      • ENT
      • Gastro
      • Orthopedics
      • Ophthalmology
      • Pulmonology
      • Surgical
      • Urology
      • Other
    • Modalities
      • Endoscopy
      • Medical Segmentation
      • Microscopy
      • Ultrasound
  • Success Stories
  • Insights
    • Magazine
    • Upcoming Events
    • Webinars
    • Meetups
    • News
    • Blog
  • The company
    • About us
    • Careers
Menu
  • Our Work
    • Fields
      • Cardiology
      • ENT
      • Gastro
      • Orthopedics
      • Ophthalmology
      • Pulmonology
      • Surgical
      • Urology
      • Other
    • Modalities
      • Endoscopy
      • Medical Segmentation
      • Microscopy
      • Ultrasound
  • Success Stories
  • Insights
    • Magazine
    • Upcoming Events
    • Webinars
    • Meetups
    • News
    • Blog
  • The company
    • About us
    • Careers
Contact

Bones and Skeleton segmentation

Computed tomography (CT) is the most common 3D process used for bone imaging. This is due to the fact that simple thresholding is usually sufficient to identify the bone tissue, based on its higher attenuation of X-rays as compared to soft tissues. The representation of a tridimensional model out of the many bidimensional CT slices has become a valuable and necessary process in many medical applications, enabling 3D observation of fractured bones and registration of other images using bones and skeleton as a region of interest.

Bones and Skeleton

However, only cortical bone offers the favorable high density needed to differentiate it from soft tissues. A large proportion of the human skeleton is made of porous spongy bone, which provides structural support and flexibility without the weight of compact bone. This spongy (or cancellous) bone offers only low X-ray attenuation, resulting in data density equal to or only slightly higher than that of soft tissues. This is even truer among elderly patients, who are the population most suffering from bones weaknesses and diseases.
The actual intensity scale used in CT as a measurement to assess bone density is the Housfield unit (HU): by definition, air has a value of -1000 HU and water has a value of 0 HU. Bones range from several hundreds to +3000 for dense bone.
Manual image editing and segmentation has long been used to extract bone geometry, but it is an extenuatingly long process with high inter-operator variability. Whatever the methods for segmenting CT images, a manual correction is usually called for in the bone contouring process.
The solution suggested by RSIP Vision utilizes state of the art deep learning models in combination with vast knowledge in computer vision to provide an algorithm that accurately segments bones in CT scans, with high speed and robustness to CT variations and artifacts.
To train this top notch AI, multiple scans were first annotated via our proprietary computer vision algorithm for initial bones segmentation, based on bone geometry, gradients and texture. After that, annotations are examined and further improved by our trained medical image analysts, working hand in hand with radiologists. With that, the optimal segmentation of bones and skeleton is achieved.
We have found this method particularly fast, regardless of whether contrast was used in the CT scans. In fact, images taken with contrast generally display blood with an intensity which is similar to bone; our technique is able to overcome this challenge and to deliver fast and satisfying bones segmentation and skeleton segmentation to our client.

Share

Share on linkedin
Share on twitter
Share on facebook

Main Field

Orthopedics

RSIP Vision brings specific field-tested experience in solving real world orthopedic imaging challenges for multiple clinical use cases. From preoperative planning, to intraoperative navigation, and many more. We are a proven leader, enabling our clients to bring innovative AI capabilities into the market.

View Orthopedics

Categories

  • Orthopedics, RSIP Vision Learns

Related Content

Knee 3D

Announcement – RSIP Vision Presents Successful Preliminary Results from Clinical Study of 2D-to-3D Knee Bones Reconstruction

Intraoperative Registration Module for Orthopedic Surgery

Announcement – Registration Module for Orthopedic Surgery

Automated Assessment of Cartilage Damage

Announcement – Automated Assessment of Cartilage Damage

Hip

Segmentation in Orthopedics with Deep Learning

Knee

Computer-Assisted Joint Replacement (hip and knee)

Surgery tool as seen on the CT scan

Navigation Systems in Orthopedic Surgery

Knee 3D

Announcement – RSIP Vision Presents Successful Preliminary Results from Clinical Study of 2D-to-3D Knee Bones Reconstruction

Intraoperative Registration Module for Orthopedic Surgery

Announcement – Registration Module for Orthopedic Surgery

Automated Assessment of Cartilage Damage

Announcement – Automated Assessment of Cartilage Damage

Hip

Segmentation in Orthopedics with Deep Learning

Knee

Computer-Assisted Joint Replacement (hip and knee)

Surgery tool as seen on the CT scan

Navigation Systems in Orthopedic Surgery

Show all

RSIP Vision

Field-tested software solutions and custom R&D, to power your next medical products with innovative AI and image analysis capabilities.

Read more about us

Get in touch

Please fill the following form and our experts will be happy to reply to you soon

Recent News

IBD Scoring – Clario, GI Reviewers and RSIP Vision Team Up

RSIP Neph Announces a Revolutionary Intra-op Solution for Partial Nephrectomy Surgeries

Announcement – RSIP Vision Presents Successful Preliminary Results from Clinical Study of 2D-to-3D Knee Bones Reconstruction

Announcement – New Urological AI Tool for 3D Reconstruction of the Ureter

All news
Upcoming Events
Stay informed for our next events
Subscribe to Our Magazines

Subscribe now and receive the Computer Vision News Magazine every month to your mailbox

 
Subscribe for free
Follow us
Linkedin Twitter Facebook Youtube

contact@rsipvision.com

Terms of Use

Privacy Policy

© All rights reserved to RSIP Vision 2023

Created by Shmulik

  • Our Work
    • title-1
      • Ophthalmology
      • Uncategorized
      • Ophthalmology
      • Pulmonology
      • Cardiology
      • Orthopedics
    • Title-2
      • Orthopedics
  • Success Stories
  • Insights
  • The company