Spark Architecture for deep learning-based dose optimization in medical imaging
Background and objectives: Deep Learning (DL) and Machine Learning (ML) have brought several breakthroughs to biomedical image analysis by making available more consistent and robust tools for the identification, classification, reconstruction, denoising, quantification, and segmentation of patterns...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291481930423X |
| _version_ | 1811273390419345408 |
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| author | Clémence Alla Takam Odette Samba Aurelle Tchagna Kouanou Daniel Tchiotsop |
| author_facet | Clémence Alla Takam Odette Samba Aurelle Tchagna Kouanou Daniel Tchiotsop |
| author_sort | Clémence Alla Takam |
| collection | DOAJ |
| description | Background and objectives: Deep Learning (DL) and Machine Learning (ML) have brought several breakthroughs to biomedical image analysis by making available more consistent and robust tools for the identification, classification, reconstruction, denoising, quantification, and segmentation of patterns in biomedical images. Recently, some applications of DL and ML in Computed Tomography (CT) scans for low dose optimization were developed. Nowadays, DL algorithms are used in CT to perform replacement of missing data (processing technique) such as low dose to high dose, sparse view to full view, low resolution to high resolution, and limited angle to full angle. Thus, DL comes with a new vision to process biomedical data imagery from CT scan. It becomes important to develop architectures and/or methods based on DL algorithms for minimizing radiation during a CT scan exam thanks to reconstruction and processing techniques. Methods: This paper describes DL for CT scan low dose optimization, shows examples described in the literature, briefly discusses new methods used in CT scan image processing, and offers conclusions. We based our study on the literature and proposed a pipeline for low dose CT scan image reconstruction. Our proposed pipeline relies on DL and the Spark Framework using MapReduce programming. We discuss our proposed pipeline with those proposed in the literature to conclude the efficiency and importance. Results: An architecture for low dose optimization using CT imagery is suggested. We used the Spark Framework to design the architecture. The proposed architecture relies on DL, and permits us to develop efficient and appropriate methods to process dose optimization with CT scan imagery. The real implementation of our pipeline for image denoising shows that we can reduce the radiation dose, and use our proposed pipeline to improve the quality of the captured image. Conclusion: The proposed architecture based on DL is complete and enables faster processing of biomedical CT imagery as compared with prior methods described in the literature. |
| first_indexed | 2024-04-12T22:58:16Z |
| format | Article |
| id | doaj.art-cf7c727a7cbe4a658d098a02a272c2d4 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-12T22:58:16Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-cf7c727a7cbe4a658d098a02a272c2d42022-12-22T03:13:07ZengElsevierInformatics in Medicine Unlocked2352-91482020-01-0119100335Spark Architecture for deep learning-based dose optimization in medical imagingClémence Alla Takam0Odette Samba1Aurelle Tchagna Kouanou2Daniel Tchiotsop3Unité de Recherche de Matière Condensée d’Electronique et de Traitement du Signal (URMACETS), Faculty of Science, University of Dschang, P.O.Box 67, Dschang, CameroonUnité de Recherche de Matière Condensée d’Electronique et de Traitement du Signal (URMACETS), Faculty of Science, University of Dschang, P.O.Box 67, Dschang, Cameroon; Service de Radiothérapie, Hôpital Général de Yaoundé-Cameroun, CameroonUnité de Recherche de Matière Condensée d’Electronique et de Traitement du Signal (URMACETS), Faculty of Science, University of Dschang, P.O.Box 67, Dschang, Cameroon; Department of Training, Research, Development and Innovation, InchTech's Solutions, Yaounde, Cameroon; Corresponding author. Unité de Recherche de Matière Condensée d’Electronique et de Traitement du Signal (URMACETS), Faculty of Science, University of Dschang, P.O.Box 67, Dschang, Cameroon.tkaurelle@gmail.comUnité de Recherche d’Automatique et d’Informatique Appliquée (URAIA), IUT-FV de Bandjoun, Université de Dschang-Cameroun, B.P. 134, Bandjoun, CameroonBackground and objectives: Deep Learning (DL) and Machine Learning (ML) have brought several breakthroughs to biomedical image analysis by making available more consistent and robust tools for the identification, classification, reconstruction, denoising, quantification, and segmentation of patterns in biomedical images. Recently, some applications of DL and ML in Computed Tomography (CT) scans for low dose optimization were developed. Nowadays, DL algorithms are used in CT to perform replacement of missing data (processing technique) such as low dose to high dose, sparse view to full view, low resolution to high resolution, and limited angle to full angle. Thus, DL comes with a new vision to process biomedical data imagery from CT scan. It becomes important to develop architectures and/or methods based on DL algorithms for minimizing radiation during a CT scan exam thanks to reconstruction and processing techniques. Methods: This paper describes DL for CT scan low dose optimization, shows examples described in the literature, briefly discusses new methods used in CT scan image processing, and offers conclusions. We based our study on the literature and proposed a pipeline for low dose CT scan image reconstruction. Our proposed pipeline relies on DL and the Spark Framework using MapReduce programming. We discuss our proposed pipeline with those proposed in the literature to conclude the efficiency and importance. Results: An architecture for low dose optimization using CT imagery is suggested. We used the Spark Framework to design the architecture. The proposed architecture relies on DL, and permits us to develop efficient and appropriate methods to process dose optimization with CT scan imagery. The real implementation of our pipeline for image denoising shows that we can reduce the radiation dose, and use our proposed pipeline to improve the quality of the captured image. Conclusion: The proposed architecture based on DL is complete and enables faster processing of biomedical CT imagery as compared with prior methods described in the literature.http://www.sciencedirect.com/science/article/pii/S235291481930423XComputer tomography scan imageDeep learningSpark frameworkArchitectureLow dose optimization |
| spellingShingle | Clémence Alla Takam Odette Samba Aurelle Tchagna Kouanou Daniel Tchiotsop Spark Architecture for deep learning-based dose optimization in medical imaging Informatics in Medicine Unlocked Computer tomography scan image Deep learning Spark framework Architecture Low dose optimization |
| title | Spark Architecture for deep learning-based dose optimization in medical imaging |
| title_full | Spark Architecture for deep learning-based dose optimization in medical imaging |
| title_fullStr | Spark Architecture for deep learning-based dose optimization in medical imaging |
| title_full_unstemmed | Spark Architecture for deep learning-based dose optimization in medical imaging |
| title_short | Spark Architecture for deep learning-based dose optimization in medical imaging |
| title_sort | spark architecture for deep learning based dose optimization in medical imaging |
| topic | Computer tomography scan image Deep learning Spark framework Architecture Low dose optimization |
| url | http://www.sciencedirect.com/science/article/pii/S235291481930423X |
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