ESTRO 2024 - Abstract Book

S2999

Physics - Autosegmentation

ESTRO 2024

[1] Sofiiuk, Konstantin, Ilya A. Petrov, and Anton Konushin. "Reviving iterative training with mask guidance for interactive segmentation." In 2022 IEEE International Conference on Image Processing (ICIP), pp. 3141-3145. IEEE, 2022. [2] Chen, Xi, Zhiyan Zhao, Yilei Zhang, Manni Duan, Donglian Qi, and Hengshuang Zhao. ‘Focalclick: Towards Practical Interactive Image Segmentation’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1300–1309, 2022. [3] Liu, Qin, Zhenlin Xu, Gedas Bertasius, and Marc Niethammer. "Simpleclick: Interactive image segmentation with simple vision transformers." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22290 22300. 2023.

[4] Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao et al. "Segment anything." arXiv preprint arXiv:2304.02643 (2023).

[5] Zhang, Kaidong, and Dong Liu. "Customized segment anything model for medical image segmentation." arXiv preprint arXiv:2304.13785 (2023).

[6] Ma, Jun, and Bo Wang. "Segment anything in medical images." arXiv preprint arXiv:2304.12306 (2023).

[7] Mazurowski, Maciej A., Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang. "Segment anything model for medical image analysis: an experimental study." Medical Image Analysis 89 (2023): 102918.

[8] Nguyen, Duy MH, Hoang Nguyen, Nghiem T. Diep, Tan N. Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda et al. "LVM Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching." arXiv preprint arXiv:2306.11925 (2023). [9] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, pp. 311-320. Springer International Publishing, 2019.

225

Digital Poster

Dosimetric assessment of the need for correction of AI generated contours for breast cancer OARs

David Nash, Shakardokht M Jafari, Antony L Palmer

Portsmouth Hospitals University NHS Trust, Medical Physics, Portsmouth, United Kingdom

Purpose/Objective:

Breast cancer is the most common cancer among women worldwide and forms a major part of the workload of all radiotherapy departments, with increasing complexity of treatment and greater efficiency in workflow needed. AI (Artificial Intelligence) generated contours offer the potential of improving the workflow. However, it is often unclear