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

Lung Nodule Classification

Lung cancer is the major cause of cancer death and the second most common cancer among men and women in the United States. It accounts for about 13% of all new cancers (more than 220,000 in the U.S. in 2015)*. Statistics on survival in people with lung cancer vary depending on the stage or extent of the cancer when it is diagnosed. Computer-aided diagnosis, aiming at performing a lung nodules classification to determine whether a nodule is malignant or benign, can assist the medical experts by making the detection process both faster and more precise, with massive life-saving consequences.
Lung cancer early detection is made difficult by the small size of pulmonary nodules, which cannot be detected by regular X-rays, and by the long time needed to classify the nodules seen on CT scans in thousands of new patients every day. Computer-aided lung nodule classification offers support to radiologists by providing automatic detection and analysis to speed and improve their manual observations.

Lung CT scans

The use of deep learning can significantly boost the performance of computer-aided diagnosis systems. We recommend the use of a deep learning features extracted from an autoencoder: autoencoders are simple learning circuits, the purpose of which is to transform inputs into outputs with the least possible amount of distortion. Though their notion is relatively simple, they play a key role in the design of machine learning processes.

Lung nodules classification

Our recommended solution starts from a bidimensional image obtained from CT scan and displaying suspicious nodules areas, as selected by the radiologist with the support given by previous biopsy, surgical inspections and other clinical sources of information. These areas are then inserted into the autoencoder and learned features are successively extracted. Following that, these features are confronted with a trained classifier to produce the lung nodules classification we are aiming at. For each nodule, two hundred dimensional features (obtained from the autoencoder) are given as input to the decision tree.
Accuracy of results is very high, though it depends on the visual similarities of nodules: the more dissimilar they are, the more reliable the lung nodule classification will be. This will also enable to keep false positives at a very low level, though of course the main concern of the study is to avoid false negatives, which would prevent the timely treatment of the disease with potentially fatal consequences. In this area, the system compellingly outperforms traditional methods. Its main breakthrough is that deep learning features also take into account the association between the different morphologic findings in the lung like presence or absence of lobulation, coarse spiculation and so on.
A very promising future direction of the research will require to integrate in the system an automatic detection of nodules with no previous use of human involvement such as radiologist-constructed lung nodule outlines.
* Source: cancer.org

Share

Share on linkedin
Share on twitter
Share on facebook

Main Field

Pulmonology

RSIP Vision’s image processing expertise in medical imaging is currently used in numerous projects, including many applications of computer vision in pulmonology. We are very proud of our contribution to lung medicine: feature detection, lung segmentation and countless other works in the pulmonary imaging field enabled our clients to give the means to physicians to save lives by giving faster and more appropriate treatment to all kind of lung diseases. You can read below about some of our breakthrough developments in Pulmonology and Bronchoscopy.

View Pulmonology

Categories

  • Pulmonology, RSIP Vision Learns

Related Content

Pulmonary embolism

Detecting Pulmonary Embolism from CT Scan

Lung vasculature segmentation

Lung tumors

Lung Tumor Segmentation

Lung Nodules Segmentation

Lung fissures

Lung Fissures Segmentation

Visible lung cancer on CT scan of chest and abdomen

Chest CT Scan Analysis with Deep Learning

Pulmonary embolism

Detecting Pulmonary Embolism from CT Scan

Lung vasculature segmentation

Lung tumors

Lung Tumor Segmentation

Lung Nodules Segmentation

Lung fissures

Lung Fissures Segmentation

Visible lung cancer on CT scan of chest and abdomen

Chest CT Scan Analysis with Deep Learning

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