A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images-Reference-Cited by-同舟云学术

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A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images

Published:2023 Issue: Volume: Page:428-440
ISSN:0302-9743
Container-title:Bioinformatics and Biomedical Engineering
language:
Short-container-title:

Author:

Kirimtat Ayca,Krejcar Ondrej

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-34960-7_30

Reference20 articles.

1. Alpar, O.: A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging. Expert Syst. Appl. 216, 119462 (2023). https://doi.org/10.1016/j.eswa.2022.119462

2. Sangui, S., Iqbal, T., Chandra, P.C., Ghosh, S.K., Ghosh, A.: 3D MRI segmentation using U-Net architecture for the detection of brain tumor. Procedia Comput. Sci. 218, 542–553 (2023). https://doi.org/10.1016/j.procs.2023.01.036

3. Altun Güven, S., Talu, M.F.: Brain MRI high-resolution image creation and segmentation with the new GAN method. Biomed. Signal Process. Control 80, 104246 (2023). https://doi.org/10.1016/j.bspc.2022.104246

4. Gab Allah, A.M., Sarhan, A.M., Elshennawy, N.M.: Edge U-Net: brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Syst. Appl. 213, 118833 (2023). https://doi.org/10.1016/j.eswa.2022.118833

5. Cao, Y., Zhou, W., Zang, M., An, D., Feng, Y., Yu, B.: MBANet: a 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images. Biomed. Signal Process. Control 80, 104296 (2023). https://doi.org/10.1016/j.bspc.2022.104296

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