Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.
Radiother Oncol
; 126(2): 312-317, 2018 02.
Article
in En
| MEDLINE
| ID: mdl-29208513
ABSTRACT
BACKGROUND AND PURPOSE:
Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. MATERIAL ANDMETHODS:
Twenty CT scans of stage I-III NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded.RESULTS:
With a median time of 20â¯min for manual contouring, the total median time saved was 7.8â¯min when using atlas-based contouring and 10â¯min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring.CONCLUSIONS:
User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiotherapy Planning, Computer-Assisted
/
Carcinoma, Non-Small-Cell Lung
/
Organs at Risk
/
Lung Neoplasms
Type of study:
Etiology_studies
/
Guideline
Limits:
Humans
Language:
En
Journal:
Radiother Oncol
Year:
2018
Document type:
Article