A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.

Hang Phuong Nguyen, Se Young Jang, Sungmin Kim

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Hang Phuong NguyenSchool of Mechanical Engineering, University of Ulsan, Ulsan, Republic of Korea.

Se Young JangDepartment of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Republic of Korea.

Sungmin KimSchool of Mechanical Engineering, University of Ulsan, Ulsan, Republic of Korea. sungminkim@ulsan.ac.kr.

Funding

The Ministry of Trade Industry & Energy (MOTIE, Korea). RS-2024-00507664

6 · The paper itself

Abstract

purposeThis study proposes a framework to address the problem of multimodal MRI-to-CT image registration by incorporating feature-based registration approach and segmentation, focusing especially on liver-specific clinical applications.

methodsThe proposed framework consists of three key stages: (1) liver segmentation using pretrained nnU-Net models for both CT and MRI images, (2) CoLlAGe features extraction within the segmented liver regions to preserve spatial information of the region of interest (ROI), and (3) image registration based on these extracted features.

resultsAcross 24 pairs of registration from MRI to CT, most of the Average Symmetric Surface Distance (ASSD) results are close to 0 millimeters, and all Dice coefficients are greater than 0.8. The values of mean ± standard deviation show that the Dice coefficient is 0.921 ± 0.038, the ASSD is 0.086 ± 0.213 mm.

conclusionThese results demonstrate the potential of the proposed framework in the study of multimodal MR-to-CT registration. The precision of liver MR-to-CT registration is considered acceptable for liver surgical applications.

Indexed as

Magnetic Resonance ImagingMultimodal ImagingTomography, X-Ray ComputedFeasibility StudiesHumansLiverCo-occurrence of local anisotropic gradient orientations (CoLlAGe) descriptorFeatures extraction for imageLearning-based segmentationMRI-to-CT image registration

Identifiers

PMID41307674

DOI10.1016/j.compmedimag.2023.102260

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.