Image-Based Correction of Continuous and Discontinuous Non-Planar Axial Distortion in Serial Section Microscopy
This addresses image quality issues in EM connectomics for researchers, but appears incremental as it builds on existing image analysis techniques without introducing a new paradigm.
The paper tackles the problem of axial distortion in serial section microscopy, which arises from imprecise physical sectioning and imaging, by developing image-based methods to identify and correct these distortions, demonstrating efficacy in proof-of-concept experiments and real-world applications.
Motivation: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order. Results: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems. Availability and Implementation: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldla/section-sort, http://github.com/saalfeldlab/em-thickness-estimation, and http://github.com/saalfeldlab/z-spacing-spark. Contact: saalfelds@janelia.hhmi.org