Deep Semantic Segmentation of Natural and Medical Images: A Review
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2019
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| _version_ | 1866910390999318528 |
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| author | Taghanaki, Saeid Asgari Abhishek, Kumar Cohen, Joseph Paul Cohen-Adad, Julien Hamarneh, Ghassan |
| author_facet | Taghanaki, Saeid Asgari Abhishek, Kumar Cohen, Joseph Paul Cohen-Adad, Julien Hamarneh, Ghassan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1910_07655 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Deep Semantic Segmentation of Natural and Medical Images: A Review Taghanaki, Saeid Asgari Abhishek, Kumar Cohen, Joseph Paul Cohen-Adad, Julien Hamarneh, Ghassan Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Deep Semantic Segmentation of Natural and Medical Images: A Review |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/1910.07655 |