Mr. Georgios Andreadis Profile

Georgios Andreadis

at Leiden Univ Medical Ctr

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Proceedings Article | 2 April 2024 Poster + Paper

A tournament of transformation models: B-Spline-based vs. mesh-based multi-objective deformable image registration

Georgios Andreadis, Joas Mulder, Anton Bouter, Peter A. Bosman, Tanja Alderliesten

Proceedings Volume 12926, 129261I (2024) https://doi.org/10.1117/12.2692876

KEYWORDS: Image registration, Deformation, Mathematical optimization, Bladder, Evolutionary optimization, Voxels, Image quality, Evolutionary algorithms, Visualization, Image restoration

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The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defining the range and realism of registrations that can be found. Two types of transformation models have emerged as popular choices: B-spline models and mesh models. Although both models have been investigated in detail, a direct comparison has not yet been made, since the models are optimized using very different optimization methods in practice. B-spline models are predominantly optimized using gradient-descent methods, while mesh models are typically optimized using finite-element method solvers or evolutionary algorithms. Multi-objective optimization methods, which aim to find a diverse set of high-quality trade-off registrations, are increasingly acknowledged to be important in deformable image registration. Since these methods search for a diverse set of registrations, they can provide a more complete picture of the capabilities of different transformation models, making them suitable for a comparison of models. In this work, we conduct the first direct comparison between B-spline and mesh transformation models, by optimizing both models with the same state-of-the-art multi-objective optimization method, the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA). The combination with B-spline transformation models, moreover, is novel. We experimentally compare both models on two different registration problems that are both based on pelvic CT scans of cervical cancer patients, featuring large deformations. Our results, on three cervical cancer patients, indicate that the choice of transformation model can have a profound impact on the diversity and quality of achieved registration outcomes.

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Multi-objective dual simplex-mesh based deformable image registration for 3D medical images - proof of concept

Georgios Andreadis, Peter A. Bosman, Tanja Alderliesten

Proceedings Volume 12032, 120322T (2022) https://doi.org/10.1117/12.2605498

KEYWORDS: Image registration, 3D image processing, 3D modeling, Medical imaging, Bladder, Optimization (mathematics), Evolutionary algorithms, Image segmentation, Image resolution, Radiotherapy

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Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration.

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