Brief review on learning-based methods for optical tomography
Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention....
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2019-11-01
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| Series: | Journal of Innovative Optical Health Sciences |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S1793545819300118 |
| _version_ | 1819002388992753664 |
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| author | Lin Zhang Guanglei Zhang |
| author_facet | Lin Zhang Guanglei Zhang |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction. |
| first_indexed | 2024-12-20T23:04:19Z |
| format | Article |
| id | doaj.art-40d39a07c8f94a6db9fb6bc5ca1449bc |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2024-12-20T23:04:19Z |
| publishDate | 2019-11-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-40d39a07c8f94a6db9fb6bc5ca1449bc2022-12-21T19:23:55ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052019-11-011261930011-11930011-1410.1142/S179354581930011810.1142/S1793545819300118Brief review on learning-based methods for optical tomographyLin Zhang0Guanglei Zhang1Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, P. R. ChinaBeijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, P. R. ChinaLearning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.http://www.worldscientific.com/doi/pdf/10.1142/S1793545819300118optical imagingtomographyinverse problemmachine learningdeep learning |
| spellingShingle | Lin Zhang Guanglei Zhang Brief review on learning-based methods for optical tomography Journal of Innovative Optical Health Sciences optical imaging tomography inverse problem machine learning deep learning |
| title | Brief review on learning-based methods for optical tomography |
| title_full | Brief review on learning-based methods for optical tomography |
| title_fullStr | Brief review on learning-based methods for optical tomography |
| title_full_unstemmed | Brief review on learning-based methods for optical tomography |
| title_short | Brief review on learning-based methods for optical tomography |
| title_sort | brief review on learning based methods for optical tomography |
| topic | optical imaging tomography inverse problem machine learning deep learning |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S1793545819300118 |
| work_keys_str_mv | AT linzhang briefreviewonlearningbasedmethodsforopticaltomography AT guangleizhang briefreviewonlearningbasedmethodsforopticaltomography |