Multi-modal matching of 2D images with 3D medical data

Image registration is the process of aligning images of the same object taken at different time points or with different imaging modalities with the aim to compare them in one coordinate system. Image registration is particularly important in biomedical imaging, where a multitude of imaging modalities exist. For example, images can be obtained with X-ray computed tomography (CT) which is based on the object’s X-ray beam attenuation whereas magnetic resonance imaging (MRI) underlines its local proton density. The gold standard in pathology for tissue analysis is histology. Histology, however, provides only 2D information in the selected sections of the 3D tissue. To evaluate the tissue’s 3D structure, volume imaging techniques, such as CT or MRI, are preferable. The combination of functional information from histology with 3D morphological data from CT is essential for tissue analysis. Furthermore, histology can validate anatomical features identified in CT data. Therefore, the registration of these two modalities is indispensable to provide a more complete overview of the tissue. Previously proposed algorithms for the registration of histological slides into 3D volumes usually rely on manual interactions, which is time-consuming and prone to bias. The high complexity of this type of registration originates from the large number of degrees of freedom. The goal of my thesis was to develop an automatic method for histology to 3D volume registration to master these challenges. The first stage of the developed algorithm uses a scale-invariant feature detector to find common matches between the histology slide and each tomography slice in a 3D dataset. A plane of the most likely position is then fitted into the feature point cloud using a robust model fitting algorithm. The second stage builds upon the first one and introduces fine-tuning of the slice position using normalized Mutual Information (NMI). Additionally, using previously developed 2D-2D registration techniques we find the rotation and translation of the histological slide within the plane. Moreover, the framework takes into account any potential nonlinear deformations of the histological slides that might occur during tissue preparation. The application of the algorithm to MRI data is investigated in our third work. The developed extension of the multi-modal feature detector showed promising results, however, the registration of a histological slide to the direct MRI volume remains a challenging task

Tissue compartmentalization enables; Salmonella; persistence during chemotherapy

Antimicrobial chemotherapy can fail to eradicate the pathogen, even in the absence of antimicrobial resistance. Persisting pathogens can subsequently cause relapsing diseases. In vitro studies suggest various mechanisms of antibiotic persistence, but their in vivo relevance remains unclear because of the difficulty of studying scarce pathogen survivors in complex host tissues. Here, we localized and characterized rare surviving; Salmonella; in mouse spleen using high-resolution whole-organ tomography. Chemotherapy cleared >99.5% of the; Salmonella; but was inefficient against a small; Salmonella; subset in the white pulp. Previous models could not explain these findings: drug exposure was adequate,; Salmonella; continued to replicate, and host stresses induced only limited; Salmonella; drug tolerance. Instead, antimicrobial clearance required support of; Salmonella; -killing neutrophils and monocytes, and the density of such cells was lower in the white pulp than in other spleen compartments containing higher; Salmonella; loads. Neutrophil densities declined further during treatment in response to receding; Salmonella; loads, resulting in insufficient support for; Salmonella; clearance from the white pulp and eradication failure. However, adjunctive therapies sustaining inflammatory support enabled effective clearance. These results identify uneven; Salmonella; tissue colonization and spatiotemporal inflammation dynamics as main causes of; Salmonella; persistence and establish a powerful approach to investigate scarce but impactful pathogen subsets in complex host environments

Histology to μCT data matching using landmarks and a density biased RANSAC

The fusion of information from different medical imaging techniques plays an important role in data analysis. Despite the many proposed registration algorithms the problem of registering 2D histological images to 3D CT or MR imaging data is still largely unsolved. In this paper we propose a computationally efficient automatic approach to match 2D histological images to 3D micro Computed Tomography data. The landmark-based approach in combination with a density-driven RANSAC plane-fitting allows efficient localization of the histology images in the 3D data within less than four minutes (single-threaded MATLAB code) with an average accuracy of 0.25 mm for correct and 2.21 mm for mismatched slices. The approach managed to successfully localize 75% of the histology images in our database. The proposed algorithm is an important step towards solving the problem of registering 2D histology sections to 3D data fully automatically

Automatic deformable registration of histological slides to {μCT} volume {3D}-Data

Localizing a histological section in the three‐dimensional dataset of a different imaging modality is a challenging 2D‐3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground‐truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset

Combined use of micro computed tomography and histology to evaluate the regenerative capacity of bone grafting materials

Pre-clinical animal models are commonly used to evaluate the osteogenic potential of bone grafting materials in-vivo. Based on the histology analysis, the currently commercially available bone grafting materials show comparable results with respect to biocompatibility, incorporation and remodeling. In the present pilot study we introduce a methodology to compare calcium phosphate-based bone grafting materials from world-leading companies in clinical trials and analyze them by means of established histology and synchrotron radiation-based micro computed tomography (SR$\mu$CT). The results indicate that the morphology of the bony structures depends on the selected bone grafting material and that an arbitrarily selected histological slice can lead to misleading conclusions. Complementary $\mu$CT data can become the basis for the identification of a representative slice. The registration of the selected histological slice with its counterpart in the three-dimensional $\mu$CT dataset was performed both visually and automatically with well comparable results. This registration allows for the compilation of a joint histogram to identify anatomical features, which can neither be extracted from histology nor from $\mu$CT data on their own. Accordingly, $\mu$CT will become an integral part of studies on the efficacy of bone augmentation materials and beyond

Automatic histology registration in application to X-ray modalities

Registration of microscope images to Computed Tomography (CT) 3D volumes is a challenging task because it requires not only multi-modal similarity measure but also 2D-3D or slice-to-volume correspondence. This type of registration is usually done manually which is very time-consuming and prone to errors. Recently we have developed the first automatic approach to localize histological sections in μCT data of a jaw bone. The median distance between the automatically found slices and the ground truth was below 35 μm. Here we explore the limitations of the method by applying it to three tomography datasets acquired with grating interferometry, laboratory-based μCT and single-distance phase retrieval. Moreover, we compare the performance of three feature detectors in the proposed framework, i.e. Speeded Up Robust Features (SURF), Scale Invariant Feature Transform (SIFT) and Affine SIFT (ASIFT). Our results show that all the feature detectors performed significantly better on the grating interferometry dataset than on other modalities. The median accuracy for the vertical position was 0.06 mm. Across the feature detector types the smallest error was achieved by the SURF-based feature detector (0.29 mm). Furthermore, the SURF-based method was computationally the most efficient. Thus, we recommend to use the SURF feature detector for the proposed framework

Importance of aspartyl protease 5 in the establishment of the intracellular niche during acute and chronic infection of Toxoplasma gondii

Virulence and persistence of the obligate intracellular parasite Toxoplasma gondii involve the secretion of effector proteins belonging to the family of dense granule proteins (GRAs) that act notably as modulators of the host defense mechanisms and participate in cyst wall formation. The subset of GRAs residing in the parasitophorous vacuole (PV) or exported into the host cell, undergo proteolytic cleavage in the Golgi upon the action of the aspartyl protease 5 (ASP5). In tachyzoites, ASP5 substrates play central roles in the morphology of the PV and the export of effectors across the translocon complex MYR1/2/3. Here, we used N-terminal amine isotopic labeling of substrates to identify novel ASP5 cleavage products by comparing the N-terminome of wild-type and Δasp5 lines in tachyzoites and bradyzoites. Validated substrates reside within the PV or PVM in an ASP5-dependent manner. Remarkably, Δasp5 bradyzoites are impaired in the formation of the cyst wall in vitro and exhibit a considerably reduced cyst burden in chronically infected animals. More specifically two-photon serial tomography of infected mouse brains revealed a comparatively reduced number and size of the cysts throughout the establishment of persistence in the absence of ASP5

Importance of aspartyl protease 5 in the establishment of the intracellular niche during acute and chronic infection of Toxoplasma gondii

Virulence and persistence of the obligate intracellular parasite Toxoplasma gondii involve the secretion of effector proteins belonging to the family of dense granule proteins (GRAs) that act notably as modulators of the host defense mechanisms and participate in cyst wall formation. The subset of GRAs residing in the parasitophorous vacuole (PV) or exported into the host cell, undergo proteolytic cleavage in the Golgi upon the action of the aspartyl protease 5 (ASP5). In tachyzoites, ASP5 substrates play central roles in the morphology of the PV and the export of effectors across the translocon complex MYR1/2/3. Here, we used N-terminal amine isotopic labeling of substrates to identify novel ASP5 cleavage products by comparing the N-terminome of wild-type and Δasp5 lines in tachyzoites and bradyzoites. Validated substrates reside within the PV or PVM in an ASP5-dependent manner. Remarkably, Δasp5 bradyzoites are impaired in the formation of the cyst wall in vitro and exhibit a considerably reduced cyst burden in chronically infected animals. More specifically two-photon serial tomography of infected mouse brains revealed a comparatively reduced number and size of the cysts throughout the establishment of persistence in the absence of ASP5. Keywords: Toxoplasma gondii; apicomplexa; aspartyl protease 5; bradyzoite; cyst burden; cyst wall; parasitophorous vacuole; protein export

Computational cell quantification in the human brain tissues based on hard X-ray phase-contrast tomograms

Cell visualization and counting plays a crucial role in biological and medical research including the study of neurodegenerative diseases. The neuronal cell loss is typically determined to measure the extent of the disease. Its characterization is challenging because the cell density and size already differs by more than three orders of magnitude in a healthy cerebellum. Cell visualization is commonly performed by histology and fluorescence microscopy. These techniques are limited to resolve complex microstructures in the third dimension. Phase-contrast tomography has been proven to provide sufficient contrast in the three-dimensional imaging of soft tissue down to the cell level and, therefore, offers the basis for the three-dimensional segmentation. Within this context, a human cerebellum sample was embedded in paraffin and measured in local phase-contrast mode at the beamline ID19 (ESRF, Grenoble, France) and the Diamond Manchester Imaging Branchline I13-2 (Diamond Light Source, Didcot, UK). After the application of Frangi-based filtering the data showed sufficient contrast to automatically identify the Purkinje cells and to quantify their density to 177 cells per mm3 within the volume of interest. Moreover, brain layers were segmented in a region of interest based on edge detection. Subsequently performed histological analysis validated the presence of the cells, which required a mapping from the two-dimensional histological slices to the three-dimensional tomogram. The methodology can also be applied to further tissue types and shows potential for the computational tissue analysis in health and disease.</p