@inproceedings{cremers_probabilistic_2007,
	title = {A probabilistic level set formulation for interactive organ segmentation},
	volume = {6512},
	url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6512/65120V/A-probabilistic-level-set-formulation-for-interactive-organ-segmentation/10.1117/12.708609.short},
	doi = {10.1117/12.708609},
	abstract = {Level set methods have become increasingly popular as a framework for image segmentation. Yet when used as a generic segmentation tool, they suffer from an important drawback: Current formulations do not allow much user interaction. Upon initialization, boundaries propagate to the final segmentation without the user being able to guide or correct the segmentation. In the present work, we address this limitation by proposing a probabilistic framework for image segmentation which integrates input intensity information and user interaction on equal footings. The resulting algorithm determines the most likely segmentation given the input image and the user input. In order to allow a user interaction in real-time during the segmentation, the algorithm is implemented on a graphics card and in a narrow band formulation.},
	urldate = {2018-06-12TZ},
	booktitle = {Medical {Imaging} 2007: {Image} {Processing}},
	publisher = {International Society for Optics and Photonics},
	author = {Cremers, Daniel and Fluck, Oliver and Rousson, Mikael and Aharon, Shmuel},
	month = mar,
	year = {2007},
	pages = {65120V}
}

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