Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

Simultaneous quantitative detection of relevant biomarkers in breast cancer by quantitative real-time PCR

The assessment of ERa, PgR and HER2 status is routinely performed today to determine the endocrine responsiveness of breast cancer samples. Such determination is usually accomplished by means of immunohistochemistry and in case of HER2 amplification by means of fluorescent in situ hybridization (FISH). The analysis of these markers can be improved by simultaneous measurements using quantitative real-time PCR (Qrt-PCR). In this study we compared Qrt-PCR results for the assessment of mRNA levels of ERa, PgR, and the members of the human epidermal growth factor receptor family, HER1, HER2, HER3 and HER4. The results were obtained in two independent laboratories using two different methods, SYBR Green I and TaqMan probes, and different primers. By linear regression we demonstrated a good concordance for all six markers. The quantitative mRNA expression levels of ERa, PgR and HER2 also strongly correlated with the respective quantitative protein expression levels prospectively detected by EIA in both laboratories. In addition, HER2 mRNA expression levels correlated well with gene amplification detected by FISH in the same biopsies. Our results indicate that both Qrt-PCR methods were robust and sensitive tools for routine diagnostics and consistent with standard methodologies. The developed simultaneous assessment of several biomarkers is fast and labor effective and allows optimization of the clinical decision-making process in breast cancer tissue and/or core biopsies.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.