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Purpose <p>This 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.</p> Methods <p>The 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.</p> Results <p>Across 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&#xa0;mm.</p> Conclusion <p>These 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Hang Phuong Nguyen,
  • Se Young Jang,
  • Sungmin Kim

摘要

Purpose

This 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.

Methods

The 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.

Results

Across 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.

Conclusion

These 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.