home
WelcomePublicationsContact
 

"Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT"

Mohamed S. Elmahdy, Tanuj Ahuja, Uulke A. van der Heide and Marius Staring

Abstract

Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning. In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging. In this work, we leverage personalized anatomical knowledge accumulated over the treatment sessions, to improve the segmentation accuracy of a pre-trained Convolution Neural Network (CNN), for a specific patient. We investigate a transfer learning approach, finetuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions. The baseline CNN model is trained on a prostate CT dataset from one hospital of 379 patients. This model is then fine-tuned and tested on an independent dataset of another hospital of 18 patients, each having 7 to 10 daily CT scans. For the prostate, seminal vesicles, bladder and rectum, the model fine-tuned on each specific patient achieved a Mean Surface Distance (MSD) of 1:64 ± 0:43 mm, 2:38 ± 2:76 mm, 2:30 ± 0:96 mm, and 1:24 ± 0:89 mm, respectively, which was significantly better than the baseline model. The proposed personalized model adaptation is therefore very promising for clinical implementation in the context of adaptive radiotherapy of prostate cancer.

 

Download

PDF (82 pages, 3023 kB) click to start download

Copyright © 2020 by the authors. Published version © 2020 by . Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Elmahdy:2020,
author = "{Mohamed S. Elmahdy and Tanuj Ahuja and Uulke A. van der Heide and Marius Staring}",
title = "{Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT}",
booktitle = "{IEEE International Symposium on Biomedical Imaging (ISBI)}",
address = "{Iowa City, Iowa, USA}",
month = "{April}",
year = "{2020}",
}

last modified: 07-01-2020 |webmaster |Copyright 2004-2020 © by Marius Staring