Performance analysis in serial-section electron microscopy image registration of neuronal tissue

Bohao Chen; Tong Xin; Hua Han; Xi Chen

doi:10.1117/12.2611465

4 April 2022 Performance analysis in serial-section electron microscopy image registration of neuronal tissue

Bohao Chen, Tong Xin, Hua Han, Xi Chen

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322N (2022) https://doi.org/10.1117/12.2611465
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Serial-section electron microscopy is a widely used technique for neuronal circuit reconstruction. However, the continuity of neuronal structure is destroyed when the tissue block is cut into a series of sections. The neuronal morphology in different sections changes with their locations in the tissue block. These content changes in adjacent sections bring a difficulty to the registration of serial electron microscopy images. As a result, the accuracy of image registration is strongly influenced by neuronal structure variation and section thickness. To evaluate registration performance, we use the spherical deformation model as a simulation of the neuron structure to analyze how registration accuracy is affected by section thickness and neuronal structure size. We mathematically describe the trend that the correlation of neuronal structures in two adjacent sections decreases with section thickness. Furthermore, we demonstrate that registration accuracy is negatively correlated with neuronal structure size and section thickness by analyzing the second-order moment of estimated translation. The experimental results of registration on synthetic data demonstrate that registration accuracy decreases with the neuronal structure size.

Citation Download Citation

Bohao Chen, Tong Xin, Hua Han, and Xi Chen "Performance analysis in serial-section electron microscopy image registration of neuronal tissue", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322N (4 April 2022); https://doi.org/10.1117/12.2611465

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