TY - GEN
T1 - Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images
AU - Naser, Mohamed A.
AU - van Dijk, Lisanne V.
AU - He, Renjie
AU - Wahid, Kareem A.
AU - Fuller, Clifton D.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Segmentation of head and neck cancer (HNC) primary tumors on medical images is an essential, yet labor-intensive, aspect of radiotherapy. PET/CT imaging offers a unique ability to capture metabolic and anatomic information, which is invaluable for tumor detection and border definition. An automatic segmentation tool that could leverage the dual streams of information from PET and CT imaging simultaneously, could substantially propel HNC radiotherapy workflows forward. Herein, we leverage a multi-institutional PET/CT dataset of 201 HNC patients, as part of the MICCAI segmentation challenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizing intensities and applying data augmentation to mitigate overfitting. Both 2D and 3D convolutional neural networks based on the U-net architecture, which were optimized with a model loss function based on a combination of dice similarity coefficient (DSC) and binary cross entropy, were implemented. The median and mean DSC values comparing the predicted tumor segmentation with the ground truth achieved by the models through 5-fold cross validation are 0.79 and 0.69 for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respectively. These promising results show potential to provide an automatic, accurate, and efficient approach for primary tumor auto-segmentation to improve the clinical practice of HNC treatment.
AB - Segmentation of head and neck cancer (HNC) primary tumors on medical images is an essential, yet labor-intensive, aspect of radiotherapy. PET/CT imaging offers a unique ability to capture metabolic and anatomic information, which is invaluable for tumor detection and border definition. An automatic segmentation tool that could leverage the dual streams of information from PET and CT imaging simultaneously, could substantially propel HNC radiotherapy workflows forward. Herein, we leverage a multi-institutional PET/CT dataset of 201 HNC patients, as part of the MICCAI segmentation challenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizing intensities and applying data augmentation to mitigate overfitting. Both 2D and 3D convolutional neural networks based on the U-net architecture, which were optimized with a model loss function based on a combination of dice similarity coefficient (DSC) and binary cross entropy, were implemented. The median and mean DSC values comparing the predicted tumor segmentation with the ground truth achieved by the models through 5-fold cross validation are 0.79 and 0.69 for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respectively. These promising results show potential to provide an automatic, accurate, and efficient approach for primary tumor auto-segmentation to improve the clinical practice of HNC treatment.
KW - Auto-contouring
KW - CT
KW - Deep learning
KW - Head and neck cancer
KW - PET
KW - Tumor segmentation
UR - http://www.scopus.com/inward/record.url?scp=85101544690&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-67194-5_10
DO - 10.1007/978-3-030-67194-5_10
M3 - Conference contribution
C2 - 33724743
AN - SCOPUS:85101544690
SN - 9783030671938
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 98
BT - Head and Neck Tumor Segmentation - First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Andrearczyk, Vincent
A2 - Oreiller, Valentin
A2 - Depeursinge, Adrien
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -