AI in Medicine Books
Published August 18, 2025 | Version 1.0
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Comprehensive Guide to U-Net for Medical Imaging: Deep Learning Architectures for Biomedical Image Segmentation

Authors/Creators

  • 1. Independent Researcher

Description

This book provides a comprehensive guide to U-Net and its applications in medical imaging. It covers deep learning architectures for biomedical image segmentation, including practical examples, explanations of neural network structures, and step-by-step implementation techniques. The book is aimed at researchers, students, and professionals interested in medical image analysis and deep learning.

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Additional details

Related works

Has part
Journal article: 10.1101/2025.08.05.25333032 (DOI)
Journal article: 10.1101/2025.08.12.25333539 (DOI)

Dates

Issued
2025-08-18
Date of publication

Audiovisual core

Capture device
Medical Imaging -Deep Learning-Biomedical Image Segmentation
Physical setting
Medical Imaging -Deep Learning-Biomedical Image Segmentation
Resource creation technique
Medical Imaging -Deep Learning-Biomedical Image Segmentation
Subject orientation
Medical Imaging -Deep Learning-Biomedical Image Segmentation
Subject part
Medical Imaging-Deep Learning-Biomedical Image Segmentation-U-Net Architecture

References

  • 1. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) (pp. 234–241). Springer. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. 3. Chollet, F. (2017). Deep learning with Python. Manning Publications. 4. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-adapting framework for U-Net-based medical image segmentation. Nature Methods, 18, 203–211. 5. Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV) (pp. 565–571). IEEE. 6. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 7. Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Cardoso, M. J. (2017). Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 240–248). Springer. 8. Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. 9. Zhang, Y., Liu, Q., & Wang, J. (2018). Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749–753. 10. Abadi, M., Agarwal, A., Barham, P., et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv preprint arXiv:1603.04467.