Lung Nodule Detection System
Principal Investigator
Name
Rudolfs Latisenko
Degrees
MSc
Institution
Thirona BV
Position Title
Senior Deep Learning Engineer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-831
Initial CDAS Request Approval
Sep 14, 2021
Title
Lung Nodule Detection System
Summary
When acquiring a chest ct scan in a general clinical setting, the radiologist examining the scan always has to report on any potentially suspicious nodules present as well as their approximate size. This can be a difficult and time consuming task. Thankfully, there are several promising machine learning techniques that could make it much easier.
In this project we aim to develop an automated system for lung nodule detection and segmentation using state-of-the-art machine learning techniques. The system is intended to be highly specific, allowing it to be used in general clinical settings and greatly reducing the workload of radiologists.
In this project we aim to develop an automated system for lung nodule detection and segmentation using state-of-the-art machine learning techniques. The system is intended to be highly specific, allowing it to be used in general clinical settings and greatly reducing the workload of radiologists.
Aims
- Develop a system for automatic pulmonary nodule detection, segmentation and measurement
- Achieve a high specificity for this system so it can aid radiologists in clinical evaluation of CT scans
Collaborators
Thirona BV