2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization
Abstract Recent developments in the registration of histology and micro-computed tomography (µCT) have broadened the perspective of pathological applications such as virtual histology based on µCT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11583-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849344216733319168 |
|---|---|
| author | Junan Chen Matteo Ronchetti Verena Stehl Van Nguyen Muhannad Al Kallaa Mahesh Thalwaththe Gedara Claudia Lölkes Stefan Moser Maximilian Seidl Matthias Wieczorek |
| author_facet | Junan Chen Matteo Ronchetti Verena Stehl Van Nguyen Muhannad Al Kallaa Mahesh Thalwaththe Gedara Claudia Lölkes Stefan Moser Maximilian Seidl Matthias Wieczorek |
| author_sort | Junan Chen |
| collection | DOAJ |
| description | Abstract Recent developments in the registration of histology and micro-computed tomography (µCT) have broadened the perspective of pathological applications such as virtual histology based on µCT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft tissue samples usually deform during the histology slide preparation, making it difficult to correlate the structures between the histology slide and µCT. In this work, we propose a novel 2D-3D multi-modal deformable image registration method. The method utilizes an initial global 2D-3D registration using an ML-based differentiable similarity measure. The registration is then finalized by an analytical out-of-plane deformation refinement. The method is evaluated on datasets acquired from tonsil and tumor tissues. µCTs of both phase-contrast and conventional absorption modalities are investigated. The registration results from the proposed method are compared with those from intensity- and keypoint-based methods. The comparison is conducted using both visual and fiducial-based evaluations. The proposed method demonstrates superior performance compared to the other two methods. |
| format | Article |
| id | doaj-art-7a2973a94d0f4fa3b3ab4e934ada6787 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a2973a94d0f4fa3b3ab4e934ada67872025-08-20T03:42:44ZengNature PortfolioScientific Reports2045-23222025-07-011511910.1038/s41598-025-11583-w2D-3D deformable image registration of histology slide and micro-CT with DISA-based initializationJunan Chen0Matteo Ronchetti1Verena Stehl2Van Nguyen3Muhannad Al Kallaa4Mahesh Thalwaththe Gedara5Claudia Lölkes6Stefan Moser7Maximilian Seidl8Matthias Wieczorek9ImFusion GmbHImFusion GmbHInstitute of Pathology, Heinrich Heine University and University Hospital of DüsseldorfImFusion GmbHInstitute of Pathology, Heinrich Heine University and University Hospital of DüsseldorfFraunhofer Institute for High-Speed Dynamics, Ernst-Mach-InstitutFraunhofer Institute for High-Speed Dynamics, Ernst-Mach-InstitutFraunhofer Institute for High-Speed Dynamics, Ernst-Mach-InstitutInstitute of Pathology, Heinrich Heine University and University Hospital of DüsseldorfImFusion GmbHAbstract Recent developments in the registration of histology and micro-computed tomography (µCT) have broadened the perspective of pathological applications such as virtual histology based on µCT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft tissue samples usually deform during the histology slide preparation, making it difficult to correlate the structures between the histology slide and µCT. In this work, we propose a novel 2D-3D multi-modal deformable image registration method. The method utilizes an initial global 2D-3D registration using an ML-based differentiable similarity measure. The registration is then finalized by an analytical out-of-plane deformation refinement. The method is evaluated on datasets acquired from tonsil and tumor tissues. µCTs of both phase-contrast and conventional absorption modalities are investigated. The registration results from the proposed method are compared with those from intensity- and keypoint-based methods. The comparison is conducted using both visual and fiducial-based evaluations. The proposed method demonstrates superior performance compared to the other two methods.https://doi.org/10.1038/s41598-025-11583-w2D-3D Image RegistrationDeformable Image RegistrationMachine LearningHistologyMicro-CT |
| spellingShingle | Junan Chen Matteo Ronchetti Verena Stehl Van Nguyen Muhannad Al Kallaa Mahesh Thalwaththe Gedara Claudia Lölkes Stefan Moser Maximilian Seidl Matthias Wieczorek 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization Scientific Reports 2D-3D Image Registration Deformable Image Registration Machine Learning Histology Micro-CT |
| title | 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization |
| title_full | 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization |
| title_fullStr | 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization |
| title_full_unstemmed | 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization |
| title_short | 2D-3D deformable image registration of histology slide and micro-CT with DISA-based initialization |
| title_sort | 2d 3d deformable image registration of histology slide and micro ct with disa based initialization |
| topic | 2D-3D Image Registration Deformable Image Registration Machine Learning Histology Micro-CT |
| url | https://doi.org/10.1038/s41598-025-11583-w |
| work_keys_str_mv | AT junanchen 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT matteoronchetti 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT verenastehl 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT vannguyen 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT muhannadalkallaa 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT maheshthalwaththegedara 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT claudialolkes 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT stefanmoser 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT maximilianseidl 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization AT matthiaswieczorek 2d3ddeformableimageregistrationofhistologyslideandmicroctwithdisabasedinitialization |