TY - GEN
T1 - Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework
AU - Ellis, David G.
AU - Aizenberg, Michele R.
N1 - Funding Information:
Acknowledgements. This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Automatic brain segmentation has the potential to save time and resources for researchers and clinicians. We aimed to improve upon previously proposed methods by implementing the U-Net model and trialing various modifications to the training and inference strategies. The trials were performed and tested on the Multimodal Brain Tumor Segmentation dataset that provides MR images of brain tumors along with manual segmentations for hundreds of subjects. The U-Net models were trained on a training set of MR images from 369 subjects and then tested against a validation set of images from 125 subjects. The proposed modifications included predicting the labeled region contours, permutations of the input data via rotation and reflection, grouping labels together, as well as creating an ensemble of models. The ensemble of models provided the best results compared to any of the other methods, but the other modifications did not demonstrate improvement. Future work will look at reducing the level of the training augmentation so that the models are better able to generalize to the validation set. Overall, our open source deep learning framework allowed us to quickly implement and test multiple U-Net training modifications. The code for this project is available at https://github.com/ellisdg/3DUnetCNN.
AB - Automatic brain segmentation has the potential to save time and resources for researchers and clinicians. We aimed to improve upon previously proposed methods by implementing the U-Net model and trialing various modifications to the training and inference strategies. The trials were performed and tested on the Multimodal Brain Tumor Segmentation dataset that provides MR images of brain tumors along with manual segmentations for hundreds of subjects. The U-Net models were trained on a training set of MR images from 369 subjects and then tested against a validation set of images from 125 subjects. The proposed modifications included predicting the labeled region contours, permutations of the input data via rotation and reflection, grouping labels together, as well as creating an ensemble of models. The ensemble of models provided the best results compared to any of the other methods, but the other modifications did not demonstrate improvement. Future work will look at reducing the level of the training augmentation so that the models are better able to generalize to the validation set. Overall, our open source deep learning framework allowed us to quickly implement and test multiple U-Net training modifications. The code for this project is available at https://github.com/ellisdg/3DUnetCNN.
KW - Brain tumor segmentation
KW - Deep learning
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85107332708&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107332708&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72087-2_4
DO - 10.1007/978-3-030-72087-2_4
M3 - Conference contribution
AN - SCOPUS:85107332708
SN - 9783030720865
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 49
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -