Patch-based nonlinear image registration for gigapixel whole slide images

Producción CientíficaImage registration of whole slide histology images allows the fusion of fine-grained information-like different immunohistochemical stains-from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods: We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results: The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance: The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level

Feature Based Registration of Brain Mr Image

ABSTRACT Medical image processing is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local intensity variations. Two main problems occurs during the registration process of non rigid image. First, the correspondence problem occurs between the template and the subject image due to variation in the voxel intensity level. Second, in the presence of bias field the occurrence of interference and noise will make the image sensitive to rotation variation. To avoid these problems and to calculate efficiently a new feature based registration of non rigid brain MR image using Uniform Pattern of Spherical Region Descriptor is proposed in this paper. The proposed method is based on a new image feature called Uniform Pattern of Spherical Region Descriptor. This uses two features namely Uniform pattern of spherical descriptor and Uniform pattern of gradient descriptor to extract geometric features from input images and to identify first order and second order voxel wise anatomical information. The MRF labeling frame work and the α-expansion algorithm are used to maximize the energy function. The defected region in the image is accurately identified by Normalized correlation method. The input image for evaluation is taken from the database Brain web and internet Brain Segmentation Repository respectively. The performance can be evaluated using Back propagation networks

Towards Ultra-High Resolution Fibre Tract Mapping of the Human Brain – Registration of Polarised Light Images and Reorientation of Fibre Vectors

Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography

Biodegradation of porous calcium phosphate scaffolds in an ectopic bone formation model studied by X-ray computed microtomograph

Three types of ceramic scaffolds with different composition and structure [namely synthetic 100% hydroxyapatite (HA; Engipore), synthetic calcium phosphate multiphase biomaterial containing 67% silicon stabilized tricalcium phosphate (Si-TCP; Skelite™) and natural bone mineral derived scaffolds (Bio-oss®)] were seeded with mesenchymal stem cells (MSC) and ectopically implanted for 8 and 16 weeks in immunodeficient mice. X-ray synchrotron radiation microtomography was used to derive 3D structural information on the same scaffolds both before and after implantation. Meaningful images and morphometric parameters such as scaffold and bone volume fraction, mean thickness and thickness distribution of the different phases as a function of the implantation time, were obtained. The used imaging algorithms allowed a direct comparison and registration of the 3D structure before and after implantation of the same sub-volume of a given scaffold. In this way it was possible to directly monitor the tissue engineered bone growth and the complete or partial degradation of the scaffold.Further, the detailed kinetics studies on Skelite™ scaffolds implanted for different length of times from 3 days to 24 weeks, revealed in the X-ray absorption histograms two separate peaks associated to HA and TCP. It was therefore possible to observe that the progressive degradation of the Skelite™ scaffolds was mainly due to the resorption of TCP. The different saturation times in the tissue engineered bone growth and in the TCP resorption confirmed that the bone growth was not limited the scaffold regions that were resorbed but continued in the inward direction with respect to the pore surface

Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections

Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid distortions produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesizing one modality from the other and then using intra-modality registration metrics, rather than by using mutual information (MI) as metric. However, such an approach typically requires a database of aligned images from the two modalities, which is very difficult to obtain for histology/MRI. Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data. In our model, the MRI slice is assumed to be a contrast-warped, spatially deformed version of the histological section. We use approximate Bayesian inference to iteratively refine the probabilistic estimate of the synthesis and the registration, while accounting for each other's uncertainty. Moreover, manually placed landmarks can be seamlessly integrated in the framework for increased performance. Experiments on a synthetic dataset show that, compared with MI, the proposed method makes it possible to use a much more flexible deformation model in the registration to improve its accuracy, without compromising robustness. Moreover, our framework also exploits information in manually placed landmarks more efficiently than MI, since landmarks inform both synthesis and registration - as opposed to registration alone. Finally, we show qualitative results on the public Allen atlas, in which the proposed method provides a clear improvement over MI based registration

Three-dimensional visualisation of soft biological structures by X-ray computed micro-tomography.

Whereas the two-dimensional (2D) visualisation of biological samples is routine, three-dimensional (3D) imaging remains a time-consuming and relatively specialised pursuit. Current commonly adopted techniques for characterising the 3D structure of non-calcified tissues and biomaterials include optical and electron microscopy of serial sections and sectioned block faces, and the visualisation of intact samples by confocal microscopy or electron tomography. As an alternative to these approaches, X-ray computed micro-tomography (microCT) can both rapidly image the internal 3D structure of macroscopic volumes at sub-micron resolutions and visualise dynamic changes in living tissues at a microsecond scale. In this Commentary, we discuss the history and current capabilities of microCT. To that end, we present four case studies to illustrate the ability of microCT to visualise and quantify: (1) pressure-induced changes in the internal structure of unstained rat arteries, (2) the differential morphology of stained collagen fascicles in tendon and ligament, (3) the development of Vanessa cardui chrysalises, and (4) the distribution of cells within a tissue-engineering construct. Future developments in detector design and the use of synchrotron X-ray sources might enable real-time 3D imaging of dynamically remodelling biological samples