Yissum - Research Development Company of the Hebrew University

Automatic Segmentation of Liver Tumors for Follow-up CT Scans

Posted by Yissum - Research Development Company of the Hebrew UniversityResponsive · Innovative Products and Technologies · Israel

Summary of the technology

Automatic Segmentation of Liver Tumors for Follow-up CT Scans

Project ID : 10-2016-4305

Yissum - Research Development Company of the Hebrew University
Yissum - Research Development Company of the Hebrew University

Description of the technology

Simplifies evaluation of treatment progress

Categories

Oncology/Cancer, Medical Applications

Development Stage

Proof of concept completed; ongoing research with larger data sets

Patent Status

Provisional patent application submitted

Highlights

  • Radiological follow-up to assess changes in size of tumors is essential during liver tumor therapy.
  • Volumetric measurements provide the most accurate information about tumor size, but tumor delineation presents a bottleneck in tumor volume computation.
  • Manual delineation is time-consuming, is user-dependent, and requires expert knowledge.
  • Automatic tumor segmentation poses significant challenges and generally processes each scan independently without taking advantage of previous scans of the same patient.

Our Innovation

New automatic algorithm for liver tumor segmentation in follow-up CT scans based on comparison with tumor delineation in a baseline scan using a Convolutional Neural Network learning technique

Illustration of the main steps of the segmentation process on two tumors (top and bottom row):(a) baseline; (b) tumor with delineation (red) on which the CNN is trained;(c) follow-up tumor with transformed baseline delineation superimposed on it. The deformable registration between the baseline and the follow-up scans is used to set the ROI that contains the follow-up tumor; (d) tumor voxel classification based on the CNN; (e) liver mask for the removal of false positives, and; (f) final segmentation after segmentation leaks removal.

Key Features

  • Enables the segmentation of a large variety of tumor types and sizes.
  • Registration between the baseline and the follow-up scan obviates the need for a separate detection step, significantly increasing robustness and accuracy.
  • Experimental results have shown a 95.4% success rate and an average overlap error of 16.8%, an improvement of 60.3% compared with standalone automatic tumor segmentation results.

Development Milestones

  • The software prototype has been completed and is being tested on a more extensive database of cases.
  • The methodology is being extended to incorporate Deep Learning methods based on Convolutional Neural Networks (CNN).
  • The next step is to install the prototype in a research hospital and integrate it in a study to collect efficacy data and time savings.

The Opportunity

  • Method may be applied to tumors in other organs and to additional imaging modalities, such as MRI
  • According to research firm MarketsandMarkets, the global market for medical image analysis software is expected to reach $3.14 billion by 2020.

Project manager

Eitan Dekel
VP Business Development - Computer Science

Project researchers

Leo Joskowicz
HUJI, School of Computer Science and Engineering
CS

Related keywords

  • Information Processing, Information System, Workflow Management
  • IT and Telematics Applications
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  • Computers
  • Computer Graphics Related
  • Specialised Turnkey Systems
  • Scanning Related
  • Peripherals
  • Computer Services
  • Computer Software Market
  • Other Computer Related
  • Computer Science & Engineering
  • medical applications

About Yissum - Research Development Company of the Hebrew University

Technology Transfer Office from Israel

Yissum Research Development Company of the Hebrew University of Jerusalem Ltd. Founded in 1964 to protect and commercialize the Hebrew University’s intellectual property. Ranked among the top technology transfer companies, Yissum has registered over 8,900 patents covering 2,500 inventions; has licensed out 800 technologies and has spun-off 90 companies. Products that are based on Hebrew University technologies and were commercialized by Yissum generate today over $2 Billion in annual sales.

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