ESTRO 2024 - Abstract Book

S3019

Physics - Autosegmentation

ESTRO 2024

HECKTOR 2022 Held Conjunction MICCAI 2022 Singap. Sept. 22 2022 Proc. Head Neck Tumor Segmentation Chall. 3rd 2022 Singapor, vol. 13626, pp. 1–30, 2023, doi: 10.1007/978-3-031-27420-6_1.

[2] Z. Wei, J. Ren, S. S. Korreman, and J. Nijkamp, “Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy,” Phys. Imaging Radiat. Oncol., vol. 25, p. 100408, Jan. 2023, doi: 10.1016/j.phro.2022.12.005.

[3] C. A. T. van den Berg and E. F. Meliadò, “Uncertainty Assessment for Deep Learning Radiotherapy Applications,” Semin. Radiat. Oncol., vol. 32, no. 4, pp. 304–318, Oct. 2022, doi: 10.1016/j.semradonc.2022.06.001.

[4] B. Kompa, J. Snoek, and A. L. Beam, “Second opinion needed: communicating uncertainty in medical machine learning,” Npj Digit. Med., vol. 4, no. 1, Art. no. 1, Jan. 2021, doi: 10.1038/s41746-020-00367-3.

[5] A. Mehrtash, W. M. Wells III, C. M. Tempany, P. Abolmaesumi, and T. Kapur, “Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation.” Jun. 29, 2020. doi: 10.1109/TMI.2020.3006437.

[6] G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin, and T. Vercauteren, “Aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks,” Neurocomputing, vol. 338, pp. 34–45, Apr. 2019, doi: 10.1016/j.neucom.2019.01.103.

[7] Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.” arXiv, Oct. 04, 2016. doi: 10.48550/arXiv.1506.02142.

[8] B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.” arXiv, Nov. 03, 2017. doi: 10.48550/arXiv.1612.01474.

[9] G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger, “Snapshot Ensembles: Train 1, get M for free.” arXiv, Mar. 31, 2017. doi: 10.48550/arXiv.1704.00109.

[10] C. F. Baumgartner et al., “PHiSeg: Capturing Uncertainty in Medical Image Segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, and A. Khan, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, pp. 119–127. doi: 10.1007/978-3-030-32245-8_14.

651

Mini-Oral

Automated per-patient confidence estimation in MRI deep learning segmentation for brain OARs

Nouf Alzahrani 1,2,3 , Ann Henry 2,4 , Bashar Al-Qaisieh 3 , Louise Murray 2,4 , Michael Nix 3,5

1 King Abdulaziz University, Diagnostic Radiology Department, Jeddah, Saudi Arabia. 2 University of Leeds, School of Medicine, Leeds, United Kingdom. 3 St. James’s University Hospital, Department of Medical Physics and Engineerin, Leeds, United Kingdom. 4 St. James’s University Hospital, Clinical Oncology, Leeds, United Kingdom. 5 University of Leeds, School of Mechanical Engineering, Leeds, United Kingdom