Deformable image registration by multi-objective optimization using a dual-dynamic transformation model to account for large anatomical differences

Some of the hardest problems in deformable image registration are problems where large anatomical differences occur between image acquisitions (e.g. large deformations due to images acquired in prone and supine positions and (dis)appearing structures between image acquisitions due to surgery). In this work we developed and studied, within a previously introduced multi-objective optimization framework, a dual-dynamic transformation model to be able to tackle such hard problems. This model consists of two non-fixed grids: one for the source image and one for the target image. By not requiring a fixed, i.e. pre-determined, association of the grid with the source image, we can accommodate for both large deformations and (dis)appearing structures. To find the transformation that aligns the source with the target image we used an advanced, powerful model-based evolutionary algorithm that exploits features of a problem's structure in a principled manner via probabilistic modeling. The actual transformation is given by the association of coordinates with each point in the two grids. Linear interpolation inside a simplex was used to extend the correspondence (i.e. transformation) as found for the grid to the rest of the volume. As a proof of concept we performed tests on both artificial and real data with disappearing structures. Furthermore, the case of prone-supine image registration for 2D axial slices of breast MRI scans was evaluated. Results demonstrate strong potential of the proposed approach to account for large deformations and (dis)appearing structures in deformable image registration

Bayesian Approach to the Brain Image Matching Problem

The application of image matching to the problem of localizing structural anatomy in images of the human brain forms the specific aim of our work. The interpretation of such images is a difficult task for human observers because of the many ways in which the identity of a given structure can be obscured. Our approach is based on the assumption that a common topology underlies the anatomy of normal individuals. To the degree that this assumption holds, the localization problem can be solved by determining the mapping from the anatomy of a given individual to some referential atlas of cerebral anatomy. Previous such approaches have in many cases relied on a physical interpretation of this mapping. In this paper, we examine a more general Bayesian formulation of the image matching problem and demonstrate the approach on two-dimensional magnetic resonance images

Automatic estimation of registration parameters : image similarity and regularization

Image registration is a procedure to spatially align two images that is often used in, for example, computer-aided diagnosis or segmentation applications. To maximize the flexibility of image registration methods, they depend on many registration parameters that must be fine-tuned for each specific application. Tuning parameters is a time-consuming task, that would ideally be performed for each individual registration. However, doing this manually for each registration is too time-consuming, and therefore we would like to do this automatically. This paper proposes a methodology to estimate one of most important parameters in a registration procedure, the regularization setting, on the basis of the image similarity. We test our method on a set of images of prostate cancer patients and show that using the proposed methodology, we can improve the result of image registration when compared to using an average-best parameter. © 2010 Copyright SPIE - The International Society for Optical Engineering

Supervised segmentation methods for the hippocampus in MR images

This study compares three different types of fully automated supervised methods for segmentation of the hippocampus in MR images. Many of such methods, trained using example data, have been presented for various medical imaging applications, but comparison of the methods is obscured because of optimization for, and evaluation on, different data. We compare three methods based on different methodological bases: atlas-based segmentation (ABS), active appearance model segmentation (AAM) and k-nearest neighbor voxel classification (KNN). All three methods are trained on 100 T1-weighted images with manual segmentations of the right hippocampus, and applied to 103 different images from the same study. Straightforward implementation of each of the three methods resulted in competitive segmentations, both mutually, as compared with methods currently reported in literature. AAM and KNN are favorable in terms of computational costs, requiring only a fraction of the time needed for ABS. The high accuracy and low computational cost make KNN the most favorable method based on this study. AAM achieves similar results as ABS in significantly less computation time. Further improvements might be achieved by fusion of the presented techniques, either methodologically or by direct fusion of the segmentation results. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

Evaluating and improving label fusion in atlas-based segmentation using the surface distance

Atlas-based segmentation is an increasingly popular method of automatically computing a segmentation. In the past, results of atlas-based segmentation have been evaluated using a volume overlap measure such as the Dice or Jaccard coefficients. However, in the first part of this paper we will argue and show that volume overlap measures are insensitive to local deviations. As a result, a segmentation that is judged to be of good quality when using such a measure may have large local deviations that may be problematic in clinical practice. In this paper, two versions of the surface distance are proposed as an alternative measure to evaluate the results of atlas-based segmentation, as they give more local information and therefore allow the detection of large local deviations. In most current atlas-based segmentation methods, the results of multiple atlases are combined to a single segmentation in a process called 'label fusion'. In a label fusion process it is important that segmentations with a high quality can be distinguished from those with a low quality. In the second part of the paper we will use the surface distance as a similarity measure during label fusion. We will present a modified version of the previously proposed SIMPLE algorithm, which selects propagated atlas segmentations based on their similarity with a preliminary estimate of the ground truth segmentation. The SIMPLE algorithm previously used the Dice coefficient as a similarity measure and in this paper we demonstrate that, using the spatial distance map instead, the results of atlas-based segmentation significantly improve. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

A guide to the BRAIN initiative cell census network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.Horizon 2020 (H2020)R01 NS096720Radiolog