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International Journal of Multimedia Information Retrieval

Erschienen in:

08.07.2022 | Trends and Surveys

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

verfasst von: Ahmed Iqbal, Muhammad Sharif, Mussarat Yasmin, Mudassar Raza, Shabib Aftab

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 3/2022

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Abstract

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

Vorheriger Artikel Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review

Nächster Artikel Semantic-enhanced discriminative embedding learning for cross-modal retrieval

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Titel: Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey
verfasst von: Ahmed Iqbal
Muhammad Sharif
Mussarat Yasmin
Mudassar Raza
Shabib Aftab
Publikationsdatum: 08.07.2022
Verlag: Springer London
Erschienen in: International Journal of Multimedia Information Retrieval / Ausgabe 3/2022
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI: https://doi.org/10.1007/s13735-022-00240-x

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