Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer
In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient’s own movement and other factors, th...
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| author | Ping Jiang Sijia Wu Wenjian Qin Yaoqin Xie |
| author_facet | Ping Jiang Sijia Wu Wenjian Qin Yaoqin Xie |
| author_sort | Ping Jiang |
| collection | DOAJ |
| description | In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient’s own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. The model uses wavelet transform to extract different components of the image and performs fusion and enhancement processing as the input to the model. The model performs multiple registrations from local to global regions. Then, we propose a novel shared pyramid registration network that can accurately extract features from different modalities, optimizing the predicted deformation field through progressive refinement. In order to improve the registration performance, we also propose a deep learning similarity measurement method combined with bistructural morphology. On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. The model was verified by the actual clinical data of cervical cancer patients. After a large number of experiments, our proposed model achieved the highest dice similarity coefficient (DSC) metric compared with the state-of-the-art registration methods. The DSC index of the MTEF algorithm is 5.64% higher than that of the TransMorph algorithm. It will effectively integrate multi-modal image information, improve the accuracy of tumor localization, and benefit more cervical cancer patients. |
| format | Article |
| id | doaj-art-e40f4b364db542648937dd028826c26d |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-e40f4b364db542648937dd028826c26d2025-08-20T02:56:05ZengMDPI AGBioengineering2306-53542024-12-011112130410.3390/bioengineering11121304Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical CancerPing Jiang0Sijia Wu1Wenjian Qin2Yaoqin Xie3Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaIn recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient’s own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. The model uses wavelet transform to extract different components of the image and performs fusion and enhancement processing as the input to the model. The model performs multiple registrations from local to global regions. Then, we propose a novel shared pyramid registration network that can accurately extract features from different modalities, optimizing the predicted deformation field through progressive refinement. In order to improve the registration performance, we also propose a deep learning similarity measurement method combined with bistructural morphology. On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. The model was verified by the actual clinical data of cervical cancer patients. After a large number of experiments, our proposed model achieved the highest dice similarity coefficient (DSC) metric compared with the state-of-the-art registration methods. The DSC index of the MTEF algorithm is 5.64% higher than that of the TransMorph algorithm. It will effectively integrate multi-modal image information, improve the accuracy of tumor localization, and benefit more cervical cancer patients.https://www.mdpi.com/2306-5354/11/12/1304cervical cancermultimodality registrationdeformation fieldmulti-levelwavelet transformation |
| spellingShingle | Ping Jiang Sijia Wu Wenjian Qin Yaoqin Xie Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer Bioengineering cervical cancer multimodality registration deformation field multi-level wavelet transformation |
| title | Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer |
| title_full | Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer |
| title_fullStr | Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer |
| title_full_unstemmed | Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer |
| title_short | Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer |
| title_sort | complex large deformation multimodality image registration network for image guided radiotherapy of cervical cancer |
| topic | cervical cancer multimodality registration deformation field multi-level wavelet transformation |
| url | https://www.mdpi.com/2306-5354/11/12/1304 |
| work_keys_str_mv | AT pingjiang complexlargedeformationmultimodalityimageregistrationnetworkforimageguidedradiotherapyofcervicalcancer AT sijiawu complexlargedeformationmultimodalityimageregistrationnetworkforimageguidedradiotherapyofcervicalcancer AT wenjianqin complexlargedeformationmultimodalityimageregistrationnetworkforimageguidedradiotherapyofcervicalcancer AT yaoqinxie complexlargedeformationmultimodalityimageregistrationnetworkforimageguidedradiotherapyofcervicalcancer |