Automatic heart segmentation using machine learning techniques
Principal Investigator
Name
Julian Bernard
Degrees
M.Sc
Institution
LARALAB UG
Position Title
Research manager
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-477
Initial CDAS Request Approval
Jan 31, 2019
Title
Automatic heart segmentation using machine learning techniques
Summary
Radiotherapy for lymphoma, breast and lung cancer induces cardiac side effects, which often manifest years after treatment and compromise therapy outcomes. It has been shown that cardiac exposure is a negative prognostic factor for patients with lung cancer after concurrent chemotherapy and radiotherapy. A precise segmentation of the heart from CT images allows to calculate cardiac radiation exposure and create comprehensive therapy plans. In this project we will develop machine learning algorithms to automatically segment the heart including cardiac substructures and peripheral vascular structures. The segmentation models shall be applied to cardiac computed tomography images as well as non-contrast CT images. A subset of data will be used to train the neural network, remaining datasets will be used to establish ground truth data with clinical experts.
Aims
1. Development of an automated heart segmentation algorithm for cardiac computed tomography
2. Development of an automated heart segmentation algorithm for non-contrast CT
Collaborators
All Laralab R&D members.