Author: Fourcade, Constance; Rubeaux, Mathieu; Mateus, Diana
Title: Using Elastix to Register Inhale/Exhale Intrasubject Thorax CT: A Unsupervised Baseline to the Task 2 of the Learn2Reg Challenge Cord-id: knrjk0v9 Document date: 2021_2_23
ID: knrjk0v9
Snippet: As part of MICCAI 2020, the Learn2Reg registration challenge was proposed as a benchmark to allow registration algorithms comparison. The task 2 of this challenge consists in intrasubject 3D HRCT inhale/exhale thorax images registration. In this context, we propose a classical iterative-based registration approach based on Elastix toolbox, optimizing normalized cross-correlation metric regularized by a bending energy penalty term. This conventional registration approach, as opposed to novel deep
Document: As part of MICCAI 2020, the Learn2Reg registration challenge was proposed as a benchmark to allow registration algorithms comparison. The task 2 of this challenge consists in intrasubject 3D HRCT inhale/exhale thorax images registration. In this context, we propose a classical iterative-based registration approach based on Elastix toolbox, optimizing normalized cross-correlation metric regularized by a bending energy penalty term. This conventional registration approach, as opposed to novel deep learning techniques, reached visually interesting results, with a target registration error of 6.55 ± 2.69 mm and a Log-Jacobian standard deviation of 0.07 ± 0.03. The code is publicly available at: https://github.com/fconstance/Learn2Reg_Task2_SimpleElastix.
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