TY - GEN
T1 - Multidimensional and multiresolution ensemble networks for brain tumor segmentation
AU - Murugesan, Gowtham Krishnan
AU - Nalawade, Sahil
AU - Ganesh, Chandan
AU - Wagner, Ben
AU - Yu, Fang F.
AU - Fei, Baowei
AU - Madhuranthakam, Ananth J.
AU - Maldjian, Joseph A.
N1 - Funding Information:
This work was partly supported by the grant, NIH/NCI U01CA207091.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components and then ensembled them to obtain robust segmentation maps. Ensembling reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from the BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrated excellent performance with DICE scores of 0.898, 0.784, 0.779 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively and an accuracy of 34.5% for predicting survival. The Ensemble of multiresolution 2D networks achieved 88.75%, 83.28% and 79.34% dice for WT, TC, and ET respectively in a test dataset of 166 subjects.
AB - In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components and then ensembled them to obtain robust segmentation maps. Ensembling reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from the BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrated excellent performance with DICE scores of 0.898, 0.784, 0.779 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively and an accuracy of 34.5% for predicting survival. The Ensemble of multiresolution 2D networks achieved 88.75%, 83.28% and 79.34% dice for WT, TC, and ET respectively in a test dataset of 166 subjects.
KW - Brain tumor segmentation
KW - Densenet-169
KW - Residual Inception Dense Networks
KW - Squeezenet
KW - Survival prediction
UR - http://www.scopus.com/inward/record.url?scp=85085475644&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085475644&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-46643-5_14
DO - 10.1007/978-3-030-46643-5_14
M3 - Conference contribution
AN - SCOPUS:85085475644
SN - 9783030466428
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 148
EP - 157
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer
T2 - 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -