Amrehn M, Steidl S, Kowarschik M, Maier A (2017)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2017
Pages Range: 1-3
Conference Proceedings Title: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)
Event location: Atlanta, Georgia
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Amrehn17-RSM.pdf
Open Access Link: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Amrehn17-RSM.pdf
In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist of drawing pictorial hints as an initial estimate of the object to extract. Only after this time consuming first phase, the efficient selective refinement of current segmentation results begins. Erroneously labeled seeds, especially near the border of the object, are challenging to detect and replace for a human and may substantially impact the overall segmentation quality. We propose an automatic seeding pipeline as well as a configuration based on saliency recognition, in order to skip the time-consuming initial interaction phase during segmentation. A median Dice score of 68.22% is reached before the first user interaction on the test data set with an error rate in seeding of only 0.088%.
APA:
Amrehn, M., Steidl, S., Kowarschik, M., & Maier, A. (2017). Robust Seed Mask Generation for Interactive Image Segmentation. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (pp. 1-3). Atlanta, Georgia, US.
MLA:
Amrehn, Mario, et al. "Robust Seed Mask Generation for Interactive Image Segmentation." Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Atlanta, Georgia 2017. 1-3.
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