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

Liver Tumor Segmentation with Deep Learning

Liver tumors, also known as hepatic tumors, are quite common and some poses a grim prognosis. Therefore, early detection and diagnosis has become a main goal for lowering mortality and morbidity.

Liver and tumors

Benign tumors include hemangiomas, adenomas, focal nodular hyperplasia (FNH). Although malignant tumors that are found in the liver are metastases of malignancies in other location, primary liver cancer is the sixth most common cancer worldwide, both in developing and industrialized countries.

Prognosis is usually poor, with low survival rates. The most common primary malignant tumor is hepatocellular carcinoma (HCC), and rarely primary tumors are cholangiocarcinoma, sarcoma or hepatoblastoma.

Treatment options include tumor resection or liver transplant. Tumors might also be ablated using radiofrequency or using cryoablation. Moreover, Transcatheter arterial chemoembolization (TACE) or Selective internal radiation therapy (SIRT) are used. In later stages, systemic treatments may be indicated.

Imaging is an integral part of diagnosis, including ultrasound (US), contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI). In addition, quality imaging of the tumor aids with choosing the correct treatment protocol and follow-up.

Liver tumors may vary in their location, shape, density, borders. Few tumors may contain calcification, fat or cystic features. Hence, identifying tumors is a difficult task by itself. Moreover, correctly classifying the tumors as malignant or benign can be grueling.

Due to the fact that there is a clear relation between liver malignancy and cirrhosis, these patients need careful and frequent evaluation and imaging in order to minimize mortality. However, differentiating between benign and malignant tumors in patients with cirrhosis can be challenging, due to distorted morphology and liver tissue enhancement. Hence the need for accurate liver tumor segmentation.

Automated Liver Tumor Segmentation

Hence, automated segmentation of liver tumors may help with quicker and more precise diagnosis and follow-up, improve surgical planning and minimize complications during tumor resections and other treatments.

RSIP Vision has developed a deep learning algorithm that reliably detects liver tumors in CT scans, overcoming the challenge posed by the large variance in tumor appearance and location across different patients. The liver tumor segmentation algorithm utilizes a sequential approach, first obtaining a coarse liver segmentation and then using it to perform a tumor segmentation focusing on the liver region. This cutting edge AI model is today’s best solution to liver tumors segmentation. Talk to a Deep Learning expert now!

Share

Share on linkedin
Share on twitter
Share on facebook

Main Field

Medical segmentation

RSIP Vision is very active in all fields of medical image processing and computer vision applications. Besides all our work in the domain of Artificial Intelligence for cardiology, ophthalmology, pulmonology and orthopedics, our engineers have contributed to many other medical segmentation projects helping our clients to improve public health and save thousands of lives. These medical applications in computer vision help physicians perform early identification of major diseases in brain, kidney, prostate and many other organs. Contact us and tell us about your medical computer vision project: we will help you complete with success all medical segmentation tasks.

View Medical segmentation

Categories

  • Medical segmentation, RSIP Vision Learns

Related Content

one-click segmentation

One-click segmentation of medical images

lymph nodes

Lymph Node Segmentation Module

Lesion Detection in CT scan (Hemorrhagic stroke)

Lesion segmentation by random-forest classifiers

Squamous cell carcinoma

Automatic segmentation of tumor cells

one-click segmentation

One-click segmentation of medical images

lymph nodes

Lymph Node Segmentation Module

Lesion Detection in CT scan (Hemorrhagic stroke)

Lesion segmentation by random-forest classifiers

Squamous cell carcinoma

Automatic segmentation of tumor cells

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