High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks
Abstract High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cr...
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| Format: | Article |
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SpringerOpen
2024-12-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-024-00246-7 |
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| author | Hong Zhang Zhikang Lu Peicong Gong Shilong Zhang Xiaoquan Yang Xiangning Li Zhao Feng Anan Li Chi Xiao |
| author_facet | Hong Zhang Zhikang Lu Peicong Gong Shilong Zhang Xiaoquan Yang Xiangning Li Zhao Feng Anan Li Chi Xiao |
| author_sort | Hong Zhang |
| collection | DOAJ |
| description | Abstract High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cross-sectional stitching, artifact removal, and signal area cropping, to meet the requirements of subsequent analyse. However, obtaining a batch of raw array mouse brain data at a resolution of $$0.65 \times 0.65 \times 3 \,\upmu \hbox {m}^{3}$$ 0.65 × 0.65 × 3 μ m 3 can reach 220TB, and the cropping of the outer contour areas in the disjointed processing still relies on manual visual observation, which consumes substantial computational resources and labor costs. In this paper, we design an efficient deep differential guided filtering module (DDGF) by fusing multi-scale iterative differential guided filtering with deep learning, which effectively refines image details while mitigating background noise. Subsequently, by amalgamating DDGF with deep learning network, we propose a lightweight deep differential guided filtering segmentation network (DDGF-SegNet), which demonstrates robust performance on our dataset, achieving Dice of 0.92, Precision of 0.98, Recall of 0.91, and Jaccard index of 0.86. Building on the segmentation, we utilize connectivity analysis for ascertaining three-dimensional spatial orientation of each brain within the array. Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques. |
| format | Article |
| id | doaj-art-9d267ece6c9f4dc3bf74c92a6851823f |
| institution | Kabale University |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-9d267ece6c9f4dc3bf74c92a6851823f2024-12-22T12:56:43ZengSpringerOpenBrain Informatics2198-40182198-40262024-12-0111111510.1186/s40708-024-00246-7High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networksHong Zhang0Zhikang Lu1Peicong Gong2Shilong Zhang3Xiaoquan Yang4Xiangning Li5Zhao Feng6Anan Li7Chi Xiao8Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan UniversityAbstract High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cross-sectional stitching, artifact removal, and signal area cropping, to meet the requirements of subsequent analyse. However, obtaining a batch of raw array mouse brain data at a resolution of $$0.65 \times 0.65 \times 3 \,\upmu \hbox {m}^{3}$$ 0.65 × 0.65 × 3 μ m 3 can reach 220TB, and the cropping of the outer contour areas in the disjointed processing still relies on manual visual observation, which consumes substantial computational resources and labor costs. In this paper, we design an efficient deep differential guided filtering module (DDGF) by fusing multi-scale iterative differential guided filtering with deep learning, which effectively refines image details while mitigating background noise. Subsequently, by amalgamating DDGF with deep learning network, we propose a lightweight deep differential guided filtering segmentation network (DDGF-SegNet), which demonstrates robust performance on our dataset, achieving Dice of 0.92, Precision of 0.98, Recall of 0.91, and Jaccard index of 0.86. Building on the segmentation, we utilize connectivity analysis for ascertaining three-dimensional spatial orientation of each brain within the array. Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques.https://doi.org/10.1186/s40708-024-00246-7High-throughput mesoscopic optical imagingDifferential guided filteringMachine learning and deep learningMouse brain data parsing |
| spellingShingle | Hong Zhang Zhikang Lu Peicong Gong Shilong Zhang Xiaoquan Yang Xiangning Li Zhao Feng Anan Li Chi Xiao High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks Brain Informatics High-throughput mesoscopic optical imaging Differential guided filtering Machine learning and deep learning Mouse brain data parsing |
| title | High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks |
| title_full | High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks |
| title_fullStr | High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks |
| title_full_unstemmed | High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks |
| title_short | High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks |
| title_sort | high throughput mesoscopic optical imaging data processing and parsing using differential guided filtered neural networks |
| topic | High-throughput mesoscopic optical imaging Differential guided filtering Machine learning and deep learning Mouse brain data parsing |
| url | https://doi.org/10.1186/s40708-024-00246-7 |
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