An efficient segment anything model for the segmentation of medical images
Author:
Funder
Major Science and Technology Innovation Project of Hangzhou
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-70288-8.pdf
Reference27 articles.
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2. Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C. & Lindseth, F. Medical image segmentation on GPUs—A comprehensive review. Med. Image Anal. 20, 1–18. https://doi.org/10.1016/j.media.2014.10.012 (2015).
3. Petitjean, C. & Dacher, J.-N. A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15, 169–184. https://doi.org/10.1016/j.media.2010.12.004 (2011).
4. Qureshi, I. et al. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. Inf. Fusion 90, 316–352. https://doi.org/10.1016/j.inffus.2022.09.031 (2023).
5. Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/TPAMI.2016.2572683 (2015).
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