2024-12-23-kamath24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Comparing the Performance of Radiation Oncologists versus a Deep Learning Dose Predictor to Estimate Dosimetric Impact of Segmentation Variations for Radiotherapy	Current evaluation methods for quality control of manual/automated tumor and organs-at- risk segmentation for radiotherapy are driven mostly by geometric correctness. It is however known that geometry-driven segmentation quality metrics cannot characterize potentially detrimental dosimetric effects of sub-optimal tumor segmentation. In this work, we build on prior studies proposing deep learning-based dose prediction models to extend its use for the task of contour quality evaluation of brain tumor treatment planning. Using a test set of 54 contour variants and their corresponding dose plans, we show that our model can be used to dosimetrically assess the quality of contours and can outperform clinical expert radiation oncologists while estimating sub-optimal situations. We compare results against three such experts and demonstrate improved accuracy in addition to time savings. Our code is available at https://github.com/ubern-mia/radonc-vs-dldp.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kamath24a	0	Comparing the Performance of Radiation Oncologists versus a Deep Learning Dose Predictor to Estimate Dosimetric Impact of Segmentation Variations for Radiotherapy	742	753	742-753	742	false	Kamath, Amith Jagannath and der Maur, Zahira Mercado Auf and Poel, Robert and Willmann, Jonas and Ermis, Ekin and Riggenbach, Elena and Andratschke, Nicolaus and Reyes, Mauricio	given family Amith Jagannath Kamath given family prefix Zahira Mercado Auf Maur der given family Robert Poel given family Jonas Willmann given family Ekin Ermis given family Elena Riggenbach given family Nicolaus Andratschke given family Mauricio Reyes	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/kamath24a/kamath24a.pdf