Open AccessAutosegmentation of lung computed tomography datasets using deep learning U-Net architecture
Author(s) -
R Prabhakar,
Akash Mehta,
Margot Lehman
Publication year - 2023
Publication title -
journal of cancer research and therapeutics/journal of cancer research and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 39
eISSN - 0973-1482
pISSN - 1998-4138
DOI - 10.4103/jcrt.jcrt_119_21
Subject(s) - medicine , lung , spinal cord , lung cancer , radiation therapy , nuclear medicine , computed tomography , segmentation , lung volumes , radiology , computer science , artificial intelligence , psychiatry
Current radiotherapy treatment techniques require a large amount of imaging data for treatment planning which demand significant clinician's time to segment target volume and organs at risk (OARs). In this study, we propose to use U-net-based architecture to segment OARs commonly encountered in lung cancer radiotherapy.