Automatic registration of serial sections of mouse lymph node by using Image-Reg

Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis

Joint analysis of multiple protein expressions and tissue morphology patterns is important for disease diagnosis, treatment planning, and drug development, requiring cross-staining alignment of multiple immunohistochemical and histopathological slides. However, cross-staining alignment of enormous gigapixel whole slide images (WSIs) at single cell precision is difficult. Apart from gigantic data dimensions of WSIs, there are large variations on the cell appearance and tissue morphology across different staining together with morphological deformations caused by slide preparation. The goal of this study is to build an image registration framework for cross-staining alignment of gigapixel WSIs of histopathological and immunohistochemical microscopic slides and assess its clinical applicability. To the authors' best knowledge, this is the first study to perform real time fully automatic cross staining alignment of WSIs with 40× and 20× objective magnification. The proposed WSI registration framework consists of a rapid global image registration module, a real time interactive field of view (FOV) localization model and a real time propagated multi-level image registration module. In this study, the proposed method is evaluated on two kinds of cancer datasets from two hospitals using different digital scanners, including a dual staining breast cancer data set with 43 hematoxylin and eosin (H&E) WSIs and 43 immunohistochemical (IHC) CK(AE1/AE3) WSIs, and a triple staining prostate cancer data set containing 30 H&E WSIs, 30 IHC CK18 WSIs, and 30 IHC HMCK WSIs. In evaluation, the registration performance is measured by not only registration accuracy but also computational time. The results show that the proposed method achieves high accuracy of 0.833 ± 0.0674 for the triple-staining prostate cancer data set and 0.931 ± 0.0455 for the dual-staining breast cancer data set, respectively, and takes only 4.34 s per WSI registration on average. In addition, for 30.23% data, the proposed method takes less than 1 s for WSI registration. In comparison with the benchmark methods, the proposed method demonstrates superior performance in registration accuracy and computational time, which has great potentials for assisting medical doctors to identify cancerous tissues and determine the cancer stage in clinical practice.

Early development of the nervous system of the eutherian <i>Tupaia belangeri</i>

Abstract. The longstanding debate on the taxonomic status of Tupaia belangeri (Tupaiidae, Scandentia, Mammalia) has persisted in times of molecular biology and genetics. But way beyond that Tupaia belangeri has turned out to be a valuable and widely accepted animal model for studies in neurobiology, stress research, and virology, among other topics. It is thus a privilege to have the opportunity to provide an overview on selected aspects of neural development and neuroanatomy in Tupaia belangeri on the occasion of this special issue dedicated to Hans-Jürg Kuhn. Firstly, emphasis will be given to the optic system. We report rather "unconventional" findings on the morphogenesis of photoreceptor cells, and on the presence of capillary-contacting neurons in the tree shrew retina. Thereafter, network formation among directionally selective retinal neurons and optic chiasm development are discussed. We then address the main and accessory olfactory systems, the terminal nerve, the pituitary gland, and the cerebellum of Tupaia belangeri. Finally, we demonstrate how innovative 3-D reconstruction techniques helped to decipher and interpret so-far-undescribed, strictly spatiotemporally regulated waves of apoptosis and proliferation which pass through the early developing forebrain and eyes, midbrain and hindbrain, and through the panplacodal primordium which gives rise to all ectodermal placodes. Based on examples, this paper additionally wants to show how findings gained from the reported projects have influenced current neuroembryological and, at least partly, medical research.

Non-rigid landmark-based large-scale image registration in 3-D reconstruction of mouse and rat kidney nephrons

BACKGROUND

Serial histological sections are suffering from mechanical distortions that disturb the reconstruction of 3-D objects. We have corrected such artifacts with a non-rigid landmark-based method that respects the original geometry in the tissue block. The method is exemplified on a large scale in the registration of semi-thin serial sections of the mouse and rat kidneys, and has been tested on FFPE-sections.

AIM

In this study of mouse and rat kidneys, we have measured and characterized the deformations introduced in the preparation of 2.5-μm-thick Epon sections and then eliminated them by a landmark-based non-rigid transformation (NRT).

METHODS

We obtained 2.5-μm-thick serial Epon sections from three mouse kidneys and three rat kidneys for 3-D reconstruction of the nephron tubules. First, the images from 3000 serial mouse and 13,000 serial rat sections underwent a classic rigid registration (CRR), and the distortions were measured and indexed. The section images underwent a further NRT in order to compensate for the deformations. The NRT used is a classic interactive landmark-based approach. The quality of the NRT was verified by comparing the geometry of the transformed images with corresponding block images.

RESULTS

After CRR, the 2.5-μm-thick sections had a linear deformation of up to 2%, the tubular lengths were overestimated with up to 1.5×, and it was most difficult to trace the tubules from section to section. After the additional NRT, the geometry of the images reflected the original geometry in the block, the tubular lengths were no longer overestimated, and the NRT highly facilitated the tracing of the tubular system.

CONCLUSIONS

NRT has facilitated the tracing of the tubular system in kidneys, a tracing, which would otherwise have been most difficult to perform. NRT has yielded substantial new knowledge to segmental and spatial nephron organization in the mouse and rat kidneys.

Contextual analysis of immunological response through whole-organ fluorescent imaging

BACKGROUND

As fluorescent microscopy has developed, significant insights have been gained into the establishment of immune response within secondary lymphoid organs, particularly in draining lymph nodes. While established techniques such as confocal imaging and intravital multi-photon microscopy have proven invaluable, they provide limited insight into the architectural and structural context in which these responses occur. To interrogate the role of the lymph node environment in immune response effectively, a new set of imaging tools taking into account broader architectural context must be implemented into emerging immunological questions.

METHODS AND RESULTS

Using two different methods of whole-organ imaging, optical clearing and three-dimensional reconstruction of serially sectioned lymph nodes, fluorescent representations of whole lymph nodes can be acquired at cellular resolution. Using freely available post-processing tools, images of unlimited size and depth can be assembled into cohesive, contextual snapshots of immunological response. Through the implementation of robust iterative analysis techniques, these highly complex three-dimensional images can be objectified into sortable object data sets. These data can then be used to interrogate complex questions at the cellular level within the broader context of lymph node biology.

CONCLUSIONS

By combining existing imaging technology with complex methods of sample preparation and capture, we have developed efficient systems for contextualizing immunological phenomena within lymphatic architecture. In combination with robust approaches to image analysis, these advances provide a path to integrating scientific understanding of basic lymphatic biology into the complex nature of immunological response.

Unsupervised content classification based nonrigid registration of differently stained histology images

Registration of histopathology images of consecutive tissue sections stained with different histochemical or immunohistochemical stains is an important step in a number of application areas, such as the investigation of the pathology of a disease, validation of MRI sequences against tissue images, multiscale physical modeling, etc. In each case, information from each stain needs to be spatially aligned and combined to ascertain physical or functional properties of the tissue. However, in addition to the gigabyte-size images and nonrigid distortions present in the tissue, a major challenge for registering differently stained histology image pairs is the dissimilar structural appearance due to different stains highlighting different substances in tissues. In this paper, we address this challenge by developing an unsupervised content classification method that generates multichannel probability images from a roughly aligned image pair. Each channel corresponds to one automatically identified content class. The probability images enhance the structural similarity between image pairs. By integrating the classification method into a multiresolution-block-matching-based nonrigid registration scheme (N. Roberts, D. Magee, Y. Song, K. Brabazon, M. Shires, D. Crellin, N. Orsi, P. Quirke, and D. Treanor, "Toward routine use of 3D histopathology as a research tool," Amer. J. Pathology, vol. 180, no. 5, 2012.), we improve the performance of registering multistained histology images. Evaluation was conducted on 77 histological image pairs taken from three liver specimens and one intervertebral disc specimen. In total, six types of histochemical stains were tested. We evaluated our method against the same registration method implemented without applying the classification algorithm (intensity-based registration) and the state-of-the-art mutual information based registration. Superior results are obtained with the proposed method.

Real-time deformable registration of multi-modal whole slides for digital pathology

Digital pathology provides new ways to visualize tissue slides and enables new workflows for analyzing these slides. Analogous to radiology, adjacent tissue sections prepared with different stains or biomarkers (e.g. H&E, IHC, special stains, or ISH; chromogenic or fluorescent) may be seen as different modalities, each representing different structural and/or functional information. Today, the anatomic pathologist views multiple glass slides using an optical microscope and then combines the information in their head to reach a (diagnostic) opinion. Moreover, due to the nature of the slide preparation and digitization process, the tissue and its features do not have the exact same morphology, appearance, or spatial alignment, making it difficult to find the same region on adjacent slides. To address such concerns, this paper presents a method for the spatial alignment of multi-modal whole slide digital microscopy images. To remain practical, the described method employs a two-step registration strategy designed to reduce computation time: the first step computes a B-spline deformable transform on low-resolution images prior to visualization, the second step applies the precomputed transformation only to the high-resolution region currently being viewed. The proposed method is demonstrated using a number of cases comprising H&E and IHC stained slides. These results indicate the feasibility of deformable registration for spatial alignment of multi-modal whole slide digital microscopy images within practical time constraints.

Accuracy of image registration between MRI and light microscopy in the ex vivo brain

A multistep procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin-stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324±0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).