Part-to-whole Registration of Histology and MRI using Shape Elements

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Non-Cancerous Abnormalities That Could Mimic Prostate Cancer Like Signal in Multi-Parametric MRI Images

Prostate Cancer (PCa) is the most common non-cutaneous cancer in North American men. Multi-parametric magnatic resonance imaging (mpMRI) has the potential to be used as a non-invasive procedure to predict locations and prognosis of PCa. This study aims to examine non-cancerous pathology lesions and normal histology that could mimic cancer in mpMRI signals. This study includes 19 radical prostatectomy specimens from the London Health Science Centre (LHSC) that were marked with 10 strand-shaped fiducials per specimen which were used as landmarks in histology processing and ex vivo MRI. Initial registration between fiducials on histology and MR images was performed followed by the development of an interactive digital technique for deformable registration of in vivo to ex vivo MRI with digital histopathology images. The relationship between MRI signals and non-cancerous abnormalities that could mimic PCa has not been tested previously in correlation with digital histopathology imaging. The unregistered mp-MRI images are contoured by 4 individual radiology observers according to the Prostate Imaging Reporting and Data System (PI-RADS). Analysis of the radiology data showed prostatic intraepithelial neoplasia (PIN), atrophy and benign prostatic hyperplasia (BPH) as main non-cancerous abnormalities responsible for cancer like signals on mpMRI. This study will help increase the accuracy of detecting PCa and play a role in the diagnosis and classification of confounders that mimic cancer in MR images

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration.

BACKGROUND: Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to develop and evaluate a protocol for deformable image registration of in-vivo to ex-vivo resected brain specimen MRI. This protocol, in conjunction with our previous work on ex-vivo to histology registration, completes a registration pipeline for histology to in-vivo MRI, enabling voxel-based validation of novel and existing MRI techniques with histopathology. NEW METHOD: A combination of image-based and landmark-based 3D registration was used to register in-vivo MRI and the ex-vivo MRI from patients (N=10) undergoing epilepsy surgery. Target registration error (TRE) was used to assess accuracy and the added benefit of deformable registration. RESULTS: A mean TRE of 1.35±0.11 and 1.41±0.33mm was found for neocortical and hippocampal specimens respectively. Statistical analysis confirmed that the deformable registration significantly improved the registration accuracy for both specimens. COMPARISON WITH EXISTING METHODS: Image registration of surgically resected brain specimens is a unique application which presents numerous technical challenges and that have not been fully addressed in previous literature. Our computed TRE are comparable to previous attempts tackling similar applications, as registering in-vivo MRI to whole brain or serial histology. CONCLUSION: The presented registration pipeline finds dense and accurate spatial correspondence between in-vivo MRI and histology and allows for the spatially local and quantitative assessment of pathological correlates in MRI

Mixed methodology in human brain research: integrating MRI and histology

Postmortem magnetic resonance imaging (MRI) can provide a bridge between histological observations and the in vivo anatomy of the human brain. Approaches aimed at the co-registration of data derived from the two techniques are gaining interest. Optimal integration of the two research fields requires detailed knowledge of the tissue property requirements for individual research techniques, as well as a detailed understanding of the consequences of tissue fixation steps on the imaging quality outcomes for both MRI and histology. Here, we provide an overview of existing studies that bridge between state-of-the-art imaging modalities, and discuss the background knowledge incorporated into the design, execution and interpretation of postmortem studies. A subset of the discussed challenges transfer to animal studies as well. This insight can contribute to furthering our understanding of the normal and diseased human brain, and to facilitate discussions between researchers from the individual disciplines

Correlation of Diffusion Tensor Imaging Indices with Histological Parameters in Rat Cervical Spinal Cord Gray Matter Following Distal Contusion Spinal Cord Injury

The purpose of this study was to delineate the diffusion tensor imaging (DTI) parameters across the cervical spinal cord gray matter (GM) in a distal (T8) rat contusion spinal cord injury (SCI) model. DTI data were obtained from ex vivo rat spinal cords and registered to corresponding histological slices in samples from the acute through chronic stages of SCI including uninjured control, 2 weeks post injury, 15 weeks post injury and 25 weeks post injury groups (n = 5 in all groups). After imaging, samples were dehydrated, blocked in paraffin, sliced axially and stained with eriochrome cyanine R stain and H&E counter-stain. A corresponding sample was post fixed with osmium tetroxide and stained with toluidine blue. Histology images of the eriochrome cyanine R stained and H&E counter-stained slices were captured at 4x and then segmented into white matter (WM) and GM and dorsal and ventral GM using a custom cluster analysis. Using whole cord templates, DTI images for each animal were then registered to the corresponding histology images. The WM and the GM regions of interest (ROI) histological templates were then used to map DTI indices, including fractional anisotropy (FA), longitudinal apparent diffusion coefficient (lADC) and transverse apparent diffusion coefficient (tADC) across the GM. The average values for each index were also calculated in predefined gray matter ROIs. Histology images of the above mentioned ROIs were captured at 40x resolution using the toluidine blue stained slices for the control and post injury groups (n=4). Motoneuron size in the ventral GM was calculated for each of the control and post injury groups. It was observed that the FA and lADC values in the dorsal GM ROI were significantly higher than that in the ventral GM ROI in controls, fifteen weeks post injury and twenty five weeks post injury groups (P \u3c 0.05). The overall GM FA value at twenty five weeks was significantly higher than the FA value at two weeks post injury (P \u3c 0.05) and the FA value in controls (P \u3c 0.05). Group analysis of the size of the motor neurons showed a 9% increase in the motoneuron size at two weeks (P \u3c 0.01) and 42% increase at twenty five weeks (P \u3c 0.01) post injury as compared to controls. The motor neurons also showed a significant increase in size at twenty five weeks post injury (P \u3c 0.01) as compared to the motor neuron size at two weeks post injury. These results indicate changes in gray matter structure rostral to a contusion injury that can be detected and monitored using DTI

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest

The Scalable Brain Atlas: instant web-based access to public brain atlases and related content

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated this project and played a crucial role in the design and quality assurance of the Scalable Brain Atla