Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis
Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/22/3448 |
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| author | Muyang Li Tuo Yao Jian Liu Ziyi Liu Zhenguo Gao Junbo Gong |
| author_facet | Muyang Li Tuo Yao Jian Liu Ziyi Liu Zhenguo Gao Junbo Gong |
| author_sort | Muyang Li |
| collection | DOAJ |
| description | Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes. |
| format | Article |
| id | doaj-art-142d021432644cc49eb935d59bf26e38 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-142d021432644cc49eb935d59bf26e382025-08-20T01:53:54ZengMDPI AGMathematics2227-73902024-11-011222344810.3390/math12223448Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image AnalysisMuyang Li0Tuo Yao1Jian Liu2Ziyi Liu3Zhenguo Gao4Junbo Gong5State Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Chemical Engineering, School of Chemical Engineering and Technology, The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, ChinaDeep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes.https://www.mdpi.com/2227-7390/12/22/3448deep learningcrystallizationin situ monitoringimage synthesisdata augmentationdata mining |
| spellingShingle | Muyang Li Tuo Yao Jian Liu Ziyi Liu Zhenguo Gao Junbo Gong Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis Mathematics deep learning crystallization in situ monitoring image synthesis data augmentation data mining |
| title | Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis |
| title_full | Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis |
| title_fullStr | Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis |
| title_full_unstemmed | Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis |
| title_short | Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis |
| title_sort | deep learning based in situ micrograph synthesis and augmentation for crystallization process image analysis |
| topic | deep learning crystallization in situ monitoring image synthesis data augmentation data mining |
| url | https://www.mdpi.com/2227-7390/12/22/3448 |
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