Real-time Image Deblurring to Improve Throughput of Serial-Section Volume Electron Microscopy for Neural Connectomic Studies

Generating large serial-section electron microscopy volumes requires automated and reliable image acquisition. In particular, image quality reliability is of paramount importance as retaking out-of-focus, or soft-focused images dramatically decreases the overall imaging throughput. As an example, when collecting a 1.3 PB data set consisting of ∼5000 serial sections, acquiring images from one section took ∼30 min and consisted of 45,750 images (∼230 million total images). Retaking 2% of these images requires breaking the automation and human intervention at least once per day. Since most low-quality images exhibit soft focus, post image correction with machine learning, may be an alternative to image re-acquisition. Out-of-focus, or blurred images arise for several reasons: a problem with the focusing algorithm, a problem with the stigmation algorithm, surface defects, or non-flat fields-of-view. Removal of image re-acquisition efforts not only improves large serial EM dataset throughput, but also improves the overall image acquisition performance of smaller datasets where labor costs, storage costs, and machine rental costs are major project considerations. To increase the throughput imaging rate of serial section EM, we have developed a machine learning-based, real-time image deblurring tool that eliminates the need for retaking out-of-focus images.

Recommended citation: R L Schalek, N Parikh, Y Wu, J W Lichtman, D Wei, Real-time Image Deblurring to Improve Throughput of Serial-Section Volume Electron Microscopy for Neural Connectomic Studies, Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 988–989, https://doi.org/10.1093/micmic/ozad067.494
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