English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Finding a realistic deformation that transforms one image into another, incase large deformations are required, is considered a key challenge in medicalimage analysis. Having a proper image registration approach to achieve thiscould unleash a number of applications requiring information to be transferredbetween images. Clinical adoption is currently hampered by many existingmethods requiring extensive configuration effort before each use, or not beingable to (realistically) capture large deformations. A recent multi-objectiveapproach that uses the Multi-Objective Real-Valued Gene-pool Optimal MixingEvolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformationmodel has shown promise, exposing the trade-offs inherent to image registrationproblems and modeling large deformations in 2D. This work builds on thispromise and introduces MOREA: the first evolutionary algorithm-basedmulti-objective approach to deformable registration of 3D images capable oftackling large deformations. MOREA includes a 3D biomechanical mesh model forphysical plausibility and is fully GPU-accelerated. We compare MOREA to twostate-of-the-art approaches on abdominal CT scans of 4 cervical cancerpatients, with the latter two approaches configured for the best results perpatient. Without requiring per-patient configuration, MOREA significantlyoutperforms these approaches on 3 of the 4 patients that represent the mostdifficult cases.

MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images