Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model
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Publisher
Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-030-98358-1_14
Reference22 articles.
1. Wang, Y., et al.: Deep attentive features for prostate segmentation in 3D transrectal ultrasound. IEEE Trans. Med. Imaging 38, 2768–2778 (2019)
2. Yan, K., Wang, X., Kim, J., Khadra, M., Fulham, M., Feng, D.: A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput Methods Programs Biomed. 170, 11–21 (2019)
3. Rundo, L., et al.: USE-Net: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365, 31–43 (2019)
4. Zhu, Y., et al.: Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. J. Magn. Reson. Imaging 49, 1149–1156 (2019)
5. Shahedi, M., Halicek, M., Guo, R., Zhang, G., Schuster, D.S., Fei, B.: A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling. Med. Phys. 45, 2527–2541 (2018)
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1. Automatic coarse-to-refinement-based ultrasound prostate segmentation using optimal polyline segment tracking method and deep learning;Applied Intelligence;2023-06-01
2. Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model;Journal of Digital Imaging;2023-02-02
3. H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve;Pattern Recognition;2022-11
4. Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression;Frontiers in Oncology;2022-06-07
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