Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Automatic Lung Lesion Detection and Segmentation in CT

Principal Investigator
Name
Lin Yang
Degrees
Ph.D.
Institution
University of Florida
Position Title
Associate Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-377
Initial CDAS Request Approval
Nov 29, 2017
Title
Automatic Lung Lesion Detection and Segmentation in CT
Summary
In this project, we will develop an automatic lung lesion detection and segmentation method using machine learning. We will use the NLST datasets as training/testing sets for various object detectors (including deep learning) and modify the best candidate to achieve promising detection accuracy. After lung lesion detection, we will develop an method for accurate lesion volume segmentation using weakly/interactive supervised learning.
Aims

1. investigate methods for automatic lesion detection,
2. develop automatic lesion segmentation on the detected lesion regions,
3. evaluate our detection and segmentation models using the NLST datasets.

Collaborators

Jinzheng Cai, University of Florida