دورية أكاديمية
Deep learning-based optical field screening for robust optical diffraction tomography
| العنوان: | Deep learning-based optical field screening for robust optical diffraction tomography |
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| المؤلفون: | DongHun Ryu, YoungJu Jo, Jihyeong Yoo, Taean Chang, Daewoong Ahn, Young Seo Kim, Geon Kim, Hyun-Seok Min, YongKeun Park |
| المصدر: | Scientific Reports, Vol 9, Iss 1, Pp 1-9 (2019) |
| بيانات النشر: | Nature Portfolio, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distil the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary-classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with >90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model’s performance and generalisability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualisation techniques that provided the saliency map underlying each model inference. We envision the proposed network would a powerful lightweight module in the tomographic reconstruction pipeline. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | https://doaj.org/toc/2045-2322Test |
| DOI: | 10.1038/s41598-019-51363-x |
| الوصول الحر: | https://doaj.org/article/9eecb8381ec241acaec2addcce5caa2aTest |
| رقم الانضمام: | edsdoj.9eecb8381ec241acaec2addcce5caa2a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-019-51363-x |