Evolutionary medical image registration using automatic parameter tuning

Valsecchi, Andrea; Dubois-Lacoste, Jérémie; Stützle, Thomas; Damas, Sergio; Santamaría, José; Marrakchi-Kacem, Linda

doi:10.1109/cec.2013.6557718

Cited by 14 publications

(12 citation statements)

References 33 publications

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“…While automatically and effectively tuning parameters for specific database/tasks is an important and active area of research (e.g., [20]–[24]), having registration algorithms that can be widely applicable to many tasks/databases is a very desirable property. This need stems not only from the size of studies, but also from the rapidly increasing number of studies undertaken in, for instance, translational neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

161

154

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Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.

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Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Akbari

Bilello

et al. 2014

IEEE Trans. Med. Imaging

161

154

View full text Add to dashboard Cite

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“…To address the challenge of parameter tuning, automated parameter tuning methods have been proposed for rigid image registration (Valsecchi et al 2013). Parameter optimization (including the weights) for DIR has been investigated and applied to computed tomography (CT) lung registration, showing that there can be large variations in the optimal values of parameters even for the same type of registration problem (Dou et al 2016).…”

Section: Related Workmentioning

confidence: 99%

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

et al. 2017

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Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

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“…The field of EC consists of genetic algorithms (GA), evolution strategies (ES), genetic programming, differential evolution, evolutionary programming and swarm intelligence. So far, the most commonly used EC methods for image registration includes GAs, memetic algorithms and particle swarm optimization [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

New Evolutionary-Based Techniques for Image Registration

Cocianu¹,

Stan²

2019

Applied Sciences

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The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.

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Evolutionary medical image registration using automatic parameter tuning

Cited by 14 publications

References 33 publications

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

New Evolutionary-Based Techniques for Image Registration

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