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Artificial Intelligence Review

Erschienen in:

13.06.2020

Deep semantic segmentation of natural and medical images: a review

verfasst von: Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh

Erschienen in: Artificial Intelligence Review | Ausgabe 1/2021

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Abstract

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.

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Titel: Deep semantic segmentation of natural and medical images: a review
verfasst von: Saeid Asgari Taghanaki
Kumar Abhishek
Joseph Paul Cohen
Julien Cohen-Adad
Ghassan Hamarneh
Publikationsdatum: 13.06.2020
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 1/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09854-1

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