TY - GEN
T1 - Quantification of uncertainty in brain tumor segmentation using generative network and bayesian active learning
AU - Alshehhi, Rasha
AU - Alshehhi, Anood
N1 - Publisher Copyright:
Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Convolutional neural networks have shown great potential in medical segmentation problems, such as braintumor segmentation. However, little consideration has been given to generative adversarial networks and uncertainty quantification over the output images. In this paper, we use the generative adversarial network to handle limited labeled images. We also quantify the modeling uncertainty by utilizing Bayesian active learning to reduce untoward outcomes. Bayesian active learning is dependent on selecting uncertain images using acquisition functions to increase accuracy. We introduce supervised acquisition functions based on distance functions between ground-truth and predicted images to quantify segmentation uncertainty. We evaluate the method by comparing it with the state-of-the-art methods based on Dice score, Hausdorff distance and sensitivity. We demonstrate that the proposed method achieves higher or comparable performance to state-of-the-art methods for brain tumor segmentation (on BraTS 2017, BraTS 2018 and BraTS 2019 datasets).
AB - Convolutional neural networks have shown great potential in medical segmentation problems, such as braintumor segmentation. However, little consideration has been given to generative adversarial networks and uncertainty quantification over the output images. In this paper, we use the generative adversarial network to handle limited labeled images. We also quantify the modeling uncertainty by utilizing Bayesian active learning to reduce untoward outcomes. Bayesian active learning is dependent on selecting uncertain images using acquisition functions to increase accuracy. We introduce supervised acquisition functions based on distance functions between ground-truth and predicted images to quantify segmentation uncertainty. We evaluate the method by comparing it with the state-of-the-art methods based on Dice score, Hausdorff distance and sensitivity. We demonstrate that the proposed method achieves higher or comparable performance to state-of-the-art methods for brain tumor segmentation (on BraTS 2017, BraTS 2018 and BraTS 2019 datasets).
KW - Bayesian Active Learning
KW - Generative Adversarial Network
KW - Segmentation
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85102987162&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102987162&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85102987162
T3 - VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 701
EP - 709
BT - VISAPP
A2 - Farinella, Giovanni Maria
A2 - Radeva, Petia
A2 - Braz, Jose
A2 - Bouatouch, Kadi
PB - SciTePress
T2 - 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021
Y2 - 8 February 2021 through 10 February 2021
ER -