DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Mobile Imaging
Current Location:
> This Story

Log in or Register to rate this News Story
Forward Printable StoryPrint Comment




CT Homepage

Why settle for less when you can have more with spectral CT? Dr. Amit Gupta describes the benefits that dual-energy spectral CT brings to radiology

More than half of patients who undergo diagnostic imaging feel anxious: survey A need for greater face-to-face interactions and consultations

From the frontlines to the frontier: CT trends and innovations Workflow is getting smarter and machine learning is changing everything

Ten-year study touts low-dose CT as standard for lung cancer screening Identify and address risk of cancer early on

New Philips MR and CT scanners debut at ASTRO CT system, Big Bore RT, and Ingenia MR-RT take aim at radiotherapy outcomes

Fujifilm enters US CT market with eye on radiotherapy treatment planning Wide bore FCT Embrace has 64- and 128-slice configurations

A dose of sophistication comes to CT protocols In 2018, dose optimization means getting everyone involved

GE to provide training to at least 140 Kenyan radiographers Partnering with Society of Radiography in Kenya

Spectral CT, workflow and dose reduction drive new CT scanner and software releases

Purchasing insight: Navigating the CT market Important considerations when it's time to shop around

Largest multi-lesion CT imaging dataset, DeepLesion, available to public

by Thomas Dworetzky , Contributing Reporter
The new publicly-accessible medical imaging database, DeepLesion, is a “critical step forward in computer-aided radiology detection, diagnosis, and deep learning,” according to the paper announcing its availability in the Journal of Medical Imaging.

It is the largest CT lesion-image database ever made available to the public, with over 32,000 annotated lesions from over 10,000 cases, according to the team from the National Institutes of Health Clinical Center that developed it. Such huge, annotated radiological datasets are essential in the creation of deep learning approaches to medical data.

Story Continues Below Advertisement


Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.

"We hope the data set will benefit the medical imaging area just as ImageNet benefited the computer vision area," said Ke Yan, the lead author on the paper and a postdoctoral fellow with senior author Dr. Ronald Summers, senior investigator and staff radiologist at the center.

DeepLesion was creating by “mining” historical medical data from the Institute's own Picture Archiving and Communication System (PACS).

“This new dataset has tremendous potential to jump-start the field of computer-aided detection (CADe) and diagnosis (CADx),” according to the release.

DeepLesion differs from most other medical image datasets now available, which are only able to spot one type of lesion, according to the NIH in a statement.

When examining CT images radiologists at the Clinical Center measure and mark clinically significant findings using “electronic bookmarks”, which can be complex and include arrows, lines, diameters, and text to pinpoint the tumor's location and size, to enable experts to spot growth or new disease.

“The bookmarks, abundant with retrospective medical data, are what scientists used to develop the DeepLesion dataset,” stated the NIH, noting that unlike most other datasets, DeepLesion has great diversity, with “all kinds of critical radiology findings from across the body, such as lung nodules, liver tumors, enlarged lymph nodes, and so on.”

The lack of such a multiple category lesion data set “has been a major roadblock to development of more universal CADe frameworks capable of detecting multiple lesion types.

This new multi-category dataset could “even enable development of CADx systems that automate radiological diagnosis,” according to the statement.

The team also created a universal lesion detector from their work on DeepLesion, and noted that while detection is time-consuming for radiologists, it is crucial to diagnosis. The thought is that this detector could be used in the future for screening by either radiologists or other CADe systems.
  Pages: 1 - 2 >>

CT Homepage

You Must Be Logged In To Post A Comment

Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Gold Service Dealer Program
Receive RFP/PS
Healthcare Providers
See all
HCP Tools
A Job
Parts Hunter +EasyPay
Get Parts
Recently Certified
View Recently
Certified Users
Recently Rated
View Recently
Certified Users
Rental Central
Rent Equipment
For Less
Sell Equipment/Parts
Get The
Most Money
Service Technicians Forum
Find Help
And Advice
Simple RFP
Get Equipment
Virtual Trade Show
Find Service
For Equipment
Access and use of this site is subject to the terms and conditions of our LEGAL NOTICE & PRIVACY NOTICE
Property of and Proprietary to, Inc. Copyright ©2001-2018, Inc.