Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis-Reference-Cited by-同舟云学术

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Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis

Published:2018-08-18 Issue: Volume: Page:229-241
ISSN:2190-3018
Container-title:Quantifying and Processing Biomedical and Behavioral Signals
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Author:

Rundo Leonardo,Militello Carmelo,Tangherloni Andrea,Russo Giorgio,Lagalla Roberto,Mauri Giancarlo,Gilardi Maria Carla,Vitabile Salvatore

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-95095-2_22

Reference28 articles.

1. Jolesz, F.A.: MRI-guided focused ultrasound surgery. Annu. Rev. Med. 60, 417–430 (2009). https://doi.org/10.1146/annurev.med.60.041707.170303

2. Wu, T., Felmlee, J.P.: A quality control program for MR-guided focused ultrasound ablation therapy. J. Appl. Clin. Med. Phys. 3(2), 162–167 (2002). https://doi.org/10.1120/jacmp.v3i2.2584

3. Cline, H.E., Schenck, J.F., Hynynen, K., Watkins, R.D., Souza, S.P., Jolesz, F.A.: MR-guided focused ultrasound surgery. J. Comput. Assist. Tomogr. 16(6), 956–965 (1992)

4. Agnello, L., Militello, C., Gagliardo, C., Vitabile, S.: Radial basis function interpolation for referenceless thermometry enhancement. In: Advances in Neural Networks—Computational and Theoretical Issues, Smart Innovation, Systems and Technologies, vol. 37, pp. 195–206. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18164-6_19

5. Buttram Jr., V.C., Reiter, R.C.: Uterine leiomyomata: etiology, symptomatology, and management. Fertil. Steril. 36(4), 433–445 (1981). https://doi.org/10.1016/S0015-0282(16)45789-4

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1. Comparative Assessment of deep learning methods for Prediction of Uterine Fibroid;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

2. Automatic segmentation of the uterus on MRI using a convolutional neural network;Computers in Biology and Medicine;2019-11

3. MedGA: A novel evolutionary method for image enhancement in medical imaging systems;Expert Systems with Applications;2019-04

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