High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks
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| Udgivet i: | Brain Informatics vol. 11, no. 1 (Dec 2024), p. 32 |
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| Udgivet: |
Springer Nature B.V.
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| Fag: | |
| Online adgang: | Citation/Abstract Full Text - PDF |
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| 001 | 3146644339 | ||
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| 022 | |a 2198-4018 | ||
| 022 | |a 2198-4026 | ||
| 024 | 7 | |a 10.1186/s40708-024-00246-7 |2 doi | |
| 035 | |a 3146644339 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 245 | 1 | |a High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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 0.65×0.65×3μm3<inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="40708_2024_246_Article_IEq1.gif" /> 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. | |
| 653 | |a Parallel processing | ||
| 653 | |a Imaging techniques | ||
| 653 | |a Message passing | ||
| 653 | |a Three dimensional analysis | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Big Data | ||
| 653 | |a Visual observation | ||
| 653 | |a Workflow | ||
| 653 | |a Efficiency | ||
| 653 | |a Brain | ||
| 653 | |a Field of view | ||
| 653 | |a Stitching | ||
| 653 | |a Background noise | ||
| 653 | |a Datasets | ||
| 653 | |a Neural networks | ||
| 653 | |a Image segmentation | ||
| 653 | |a Image filters | ||
| 653 | |a Pipelining (computers) | ||
| 653 | |a Arrays | ||
| 653 | |a Optical data processing | ||
| 653 | |a Connectivity analysis | ||
| 773 | 0 | |t Brain Informatics |g vol. 11, no. 1 (Dec 2024), p. 32 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3146644339/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3146644339/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |