2136669
Project Grant
Overview
Grant Description
SBIR Phase I: AI-Assisted Software for Fast Labeling of Medical Tomographic Images - The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to extract new valuable information from medical images, accelerate image interpretation by radiologists, and improve patient outcomes.
The process of identifying key features in images, known as "labeling", is the key to improved diagnosis and management of certain conditions. The innovations proposed here will substantially lower the cost of labeled datasets, enabling access for developers of artificial intelligence (AI) algorithms and improving the use of AI in health care.
This Small Business Innovation Research (SBIR) Phase I project will apply machine learning algorithms to develop a system for assisting in manual labeling of medical tomographic images. The proposed research will result in an adaptive system architecture that evolves to accelerate labeling and increase the volume of labeled data. Moreover, the research will increase labeling accuracy at the edges of anatomical structures.
For instance, surgical resections for cancer treatment require accurate labeling of the edges of abnormal tissue to ensure clean margins and minimal recurrence. Similarly, radiation therapy planning requires accurate labeling of the edges of organs at risk for safety and favorable outcomes. Due to its clinical importance, accurate manual labeling of ambiguities and sophisticated shapes is highly time-consuming.
The proposed approach is differentiated from current methods by the inclusion of an additional subsystem for increasing the accuracy of edge labeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
The process of identifying key features in images, known as "labeling", is the key to improved diagnosis and management of certain conditions. The innovations proposed here will substantially lower the cost of labeled datasets, enabling access for developers of artificial intelligence (AI) algorithms and improving the use of AI in health care.
This Small Business Innovation Research (SBIR) Phase I project will apply machine learning algorithms to develop a system for assisting in manual labeling of medical tomographic images. The proposed research will result in an adaptive system architecture that evolves to accelerate labeling and increase the volume of labeled data. Moreover, the research will increase labeling accuracy at the edges of anatomical structures.
For instance, surgical resections for cancer treatment require accurate labeling of the edges of abnormal tissue to ensure clean margins and minimal recurrence. Similarly, radiation therapy planning requires accurate labeling of the edges of organs at risk for safety and favorable outcomes. Due to its clinical importance, accurate manual labeling of ambiguities and sophisticated shapes is highly time-consuming.
The proposed approach is differentiated from current methods by the inclusion of an additional subsystem for increasing the accuracy of edge labeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Rockville,
Maryland
20852-1708
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Alienbyte Scientific Software was awarded
Project Grant 2136669
worth $255,807
from Directorate for Technology, Innovation and Partnerships in February 2022 with work to be completed primarily in Rockville Maryland United States.
The grant
has a duration of 5 months and
was awarded through assistance program 47.041 Engineering.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:AI-assisted software for fast labeling of medical tomographic images
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to extract new valuable information from medical images, accelerate image interpretation by radiologists, and improve patient outcomes. The process of identifying key features in images, known as ``labeling'', is the key to improved diagnosis and management of certain conditions.The innovations proposed here will substantially lower the cost of labeled datasets, enabling access for developers of artificial intelligence (AI) algorithms and improving the use of AI in health care.This Small Business Innovation Research (SBIR) Phase I project will apply machine learning algorithms to develop a system for assisting in manual labeling of medical tomographic images. The proposed research will result in an adaptive system architecture that evolves to accelerate labeling and increase the volume of labeled data. Moreover, the research will increase labeling accuracy at the edges of anatomical structures. For instance, surgical resections for cancer treatment requires accurate labeling of the edges of abnormal tissue to ensure clean margins and minimal recurrence. Similarly, radiation therapy planning requires accurate labeling of the edges of organs at risk for safety and favorable outcomes. Due to its clinical importance, accurate manual labeling of ambiguities and sophisticated shapes is highly time-consuming. The proposed approach is differentiated from current methods by the inclusion of an additional subsystem for increasing the accuracy of edge labeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 21-562
Status
(Complete)
Last Modified 2/18/22
Period of Performance
2/15/22
Start Date
7/31/22
End Date
Funding Split
$255.8K
Federal Obligation
$0.0
Non-Federal Obligation
$255.8K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2136669
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
490707 DIVISION OF INDUSTRIAL INNOVATION
Funding Office
490707 DIVISION OF INDUSTRIAL INNOVATION
Awardee UEI
MQ2SS1Z33878
Awardee CAGE
8JDE6
Performance District
08
Senators
Benjamin Cardin
Chris Van Hollen
Chris Van Hollen
Representative
Jamie Raskin
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $255,807 | 100% |
Modified: 2/18/22