Reference-free Three-Dimensional Reconstruction of Digitized Histological Slice Sequences - Gaffling, Simone (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
Although first 3-D reconstructions of minute structures were made over 130 years ago, the need to understand the shape and morphology of various specimens at the microscopic level has increased significantly in the last decade. Since volumetric imaging techniques like computed tomography or magnetic resonance imaging are still limited regarding spatial resolution and availability, an interest in the digital reconstruction of histological tissue preparations prepared and stained according to the scientific question has evolved. One fundamental technique in 3D histology reconstruction is the use of image registration for alignment of slice images and reversal of slice deformations. Here, the individual registration components are often chosen to best fit the data at hand. This thesis uses a rigid transformation model for simple alignment and a non-rigid, non-parametric image transform for image unwarping. The core principle of the unwarping strategy is the iterative Gauss-Seidel method, which is capable of selectively eliminating higher frequency errors from given functions consisting of superimposed signals of different wavelengths. Evaluation strategies like the Sum of Squared Distances, Target Registration Error and Graylevel Cooccurrence Matrices are ways to assess the quality of match between slices and the coherence of reconstructed histology volumes. The histological preparation procedure steps like resection, embedding, cutting and staining significantly destroy the tissue, and the digitization step leads to disambiguities and information loss regarding the shape of structures. The image data sets are therefore inherently afflicted with a large number of artifacts like intensity variances, foldings, deformations or missing parts. The preprocessing and reconstruction pipeline starts with the gray value conversion of color images to reduce the computational effort. As one of the two main contributions of the work a novel intensity standardization for histological image sequences is proposed. Here, percentile values of individual slice histograms are determined, and corresponding percentile values treated as list of function values. The Gauss-Seidel method is used to eliminate the higher frequency intensity variances, but preserve the lower frequency differences stemming from changing tissue content. For an entire slice data set, a mean intensity correction of 0.45 and mean standard deviation of correction of only 5.03 – half of the correction other methods achieved – demonstrates a moderate adaption of the original intensity values, while qualitative results show good contrast and smooth appearance. After optional replacement of defective or missing slice images and possibly manual sorting of the image data set, a first simple 3D reconstruction is achieved by rigid image registration and stacking. The remaining nonlinear slice deformations prevent real coherence of the anatomy. The second main contribution of the work is the adaptation of the Gauss-Seidel method for image sequences. While the non-rigid image registration process provides a possibility to emulate the slice deformations and thus to reverse them, the Gauss- Seidel method determines how these deformations are eliminated by an iterative update of the individual images, taking into account their local neighborhood. The method is suitable for large datasets, since it does not require the entire dataset in memory. Experiments show that the method converges quickly, and experiments with synthetic data with known deformations suggest that the true deformation of the slices is effectively reversed, with the Mean Target Registration Error being below 1 pixel offset to the original CT data set after 5 iterations. Visually, the results are convincing and provide instructive insights into microscopic morphology. Further improvements of the proposed reconstruction pipeline include a gray level conversion based on stain protocol or structure-of-interest, better defect slice replacement methods, or automated methods to restore the correct slice sequence. The intensity matching method could be adapted based on the tissue content. For large data sets, the Gauss-Seidel unwarping method should be modified to a multilevel approach. Possible applications include better anatomical teaching, improvement of in-vivo 3D imaging protocols or digital pathology.<br
Keywords: Gauss-Seidel Method for Deformation Reversal; Histology Reconstruction; Histology Sequences; Reconstruction