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PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT
Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review
DOI
- https://doi.org/10.1007/978-3-030-98253-9_7
Final published version
- Jintao Ren
- Bao Ngoc Huynh, Norwegian University of Life Sciences ,
- Aurora Rosvoll Groendahl, Norwegian University of Life Sciences ,
- Oliver Tomic, Norwegian University of Life Sciences ,
- Cecilia Marie Futsaether, Norwegian University of Life Sciences ,
- Stine Sofia Korreman
Auto-segmentation of head and neck cancer (HNC) primary gross tumor volume (GTVt) is a necessary but challenging process for radiotherapy treatment planning and radiomics studies. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and the prediction of patient outcomes. In this paper, we focus on the segmentation part by proposing two PET normalization methods to mitigate impacts from intensity variances between PET scans for deep learning-based GTVt auto-segmentation. We also compared the performance of three popular hybrid loss functions. An ensemble of our proposed models achieved an average Dice Similarity Coefficient (DSC) of 0.779 and median 95% Hausdorff Distance (HD95) of 3.15 mm on the test set. Team: Aarhus_Oslo.
| Original language | English |
|---|---|
| Title of host publication | Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings |
| Editors | Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge |
| Number of pages | 9 |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Publication year | 2022 |
| Pages | 83-91 |
| ISBN (print) | 9783030982522 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sept 2021 → 27 Sept 2021 |
Conference
| Conference | 2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 |
|---|---|
| By | Virtual, Online |
| Periode | 27/09/2021 → 27/09/2021 |
| Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13209 LNCS |
| ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
- Auto-segmentation, Deep learning, Gross tumor volume, Head and neck cancer
Research areas
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