Serial section registration of axonal confocal microscopy datasets for long-range neural circuit reconstruction

Diffeomorphic Multi-Frame Non-Rigid Registration of Cell Nuclei in 2D and 3D Live Cell Images

To gain a better understanding of cellular and molecular processes, it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. Since, generally, the subcellular particles move and cell nuclei move as well as deform, it is important to decouple the movement of particles from that of the cell nuclei using non-rigid registration methods. We have developed a diffeomorphic multi-frame approach for non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images. Our non-rigid registration approach is based on local optic flow estimation, exploits information from multiple consecutive image frames, and determines diffeomorphic transformations in the log-domain, which allows efficient computation of the inverse transformations. To register single images of an image sequence to a reference image, we use a temporally weighted mean image, which is constructed based on inverse transformations and multiple consecutive frames. Using multiple consecutive frames improves the registration accuracy compared to pairwise registration, and using a temporally weighted mean image significantly reduces the computation time compared with previous work. In addition, we use a flow boundary preserving method for regularization of computed deformation vector fields, which prevents from over-smoothing compared to standard Gaussian filtering. Our approach has been successfully applied to 2D and 3D synthetic as well as real live cell microscopy image sequences, and an experimental comparison with non-rigid pairwise, multi-frame, and temporal groupwise registration has been carried out.

Automated stitching of microtubule centerlines across serial electron tomograms

Tracing microtubule centerlines in serial section electron tomography requires microtubules to be stitched across sections, that is lines from different sections need to be aligned, endpoints need to be matched at section boundaries to establish a correspondence between neighboring sections, and corresponding lines need to be connected across multiple sections. We present computational methods for these tasks: 1) An initial alignment is computed using a distance compatibility graph. 2) A fine alignment is then computed with a probabilistic variant of the iterative closest points algorithm, which we extended to handle the orientation of lines by introducing a periodic random variable to the probabilistic formulation. 3) Endpoint correspondence is established by formulating a matching problem in terms of a Markov random field and computing the best matching with belief propagation. Belief propagation is not generally guaranteed to converge to a minimum. We show how convergence can be achieved, nonetheless, with minimal manual input. In addition to stitching microtubule centerlines, the correspondence is also applied to transform and merge the electron tomograms. We applied the proposed methods to samples from the mitotic spindle in C. elegans, the meiotic spindle in X. laevis, and sub-pellicular microtubule arrays in T. brucei. The methods were able to stitch microtubules across section boundaries in good agreement with experts' opinions for the spindle samples. Results, however, were not satisfactory for the microtubule arrays. For certain experiments, such as an analysis of the spindle, the proposed methods can replace manual expert tracing and thus enable the analysis of microtubules over long distances with reasonable manual effort.