A Gauss-Seidel iteration scheme for reference-free 3-D histological image reconstruction.

Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.

NON-RIGID REGISTRATION GUIDED BY LANDMARKS AND LEARNING.

Registration methods frequently rely on prior information in order to generate anatomical meaningful transformations between medical scans. In this paper, we propose a novel intensity based non-rigid registration framework, which is guided by landmarks and a regularizer based on Principle Component Analysis (PCA). Unlike existing methods in this domain, the computational complexity of our approach reduces with the number of landmarks. Furthermore, our PCA is invariant to translations. The additional regularizer is based on the outcome of this PCA. We register a skull CT scan to MR scans aquired by a MR/PET hybrid scanner. This aligned CT scan can then be used to gain an attenuation map for PET reconstruction. As a result we have a Dice coefficient for bone areas at 0.71 and a Dice coefficient for bone and soft issue areas at 0.97.

Automatic parameter selection for multimodal image registration.

Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CT-MR, PET-MR, and MR-MR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion-corrected magnetic resonance images.

Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration.

Active shape models (ASMs) are widely used for applications in the field of image segmentation. Building an ASM requires to determine point correspondences for input training data, which usually results in a set of landmarks distributed according to the statistical variations. State-of-the-art methods solve this problem by minimizing the description length of all landmarks using a parametric mapping of the target shape (e.g. a sphere). In case of models composed of multiple sub-parts or highly non-convex shapes, these techniques feature substantial drawbacks. This article proposes a novel technique for solving the crucial correspondence problem using non-rigid image registration. Unlike existing approaches the new method yields more detailed ASMs and does not require explicit or parametric formulations of the problem. Compared to other methods, the already built ASM can be updated with additional prior knowledge in a very efficient manner. For this work, a training set of 3-D kidney pairs has been manually segmented from 41 CT images of different patients and forms the basis for a clinical evaluation. The novel registration based approach is compared to an already established algorithm that uses a minimum description length (MDL) formulation. The presented results indicate that the use of non-rigid image registration to solve the point correspondence problem leads to improved ASMs and more accurate segmentation results. The sensitivity could be increased by approximately 10%. Experiments to analyze the dependency on the user initialization also show a higher sensitivity of 5-15%. The mean squared error of the segmentation results and the ground truth manually classified data could also be reduced by 20-34% with respect to varying numbers of training samples.