Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model
Abstract Background Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that ca...
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BMC
2025-02-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-025-02603-0 |
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| author | Yi Guo Jun Chen Lin Lu Lingna Qiu Linzhen Lan Feibao Guo Jinsheng Hong |
| author_facet | Yi Guo Jun Chen Lin Lu Lingna Qiu Linzhen Lan Feibao Guo Jinsheng Hong |
| author_sort | Yi Guo |
| collection | DOAJ |
| description | Abstract Background Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that can exist between multimodal images. The purpose of this study is to improve the registration accuracy between CT and MR for nasopharyngeal carcinoma cases. Methods 269 cases were enrolled in the study, and 188 cases were designated for training, while a separate set of 81 cases was reserved for testing. Each case had a CT volume and a T1-MR volume. The treatment table was removed from their CT images. The CycleFCNs model was used for deformable registration, and two strategies including adaptive mask registration strategy and weight allocation strategy were adopted for training. Dice similarity coefficient, Hausdorff distance, precision, and recall were calculated for normal tissues of CT-MR image pairs, before and after the registration. Three deformable registration methods including RayStation, Elastix, and VoxelMorph were compared with the proposed method. Results The registration results of RayStation and Elastix are essentially consistent. Upon employing the VoxelMorph model and the proposed method for registration, a clear trend of increased dice similarity coefficient and decreased hausdorff distance can be observed. It is noteworthy that for the temporomandibular joint, pituitary, optic nerve, and optic chiasma, the proposed method has improved the average dice similarity coefficient from 0.86 to 0.91, 0.87 to 0.93, 0.85 to 0.89, and 0.77 to 0.83, respectively, as compared to RayStation. Additionally, within the same anatomical structures, the average hausdorff distance has been decreased from 2.98 mm to 2.28 mm, 1.83 mm to 1.53 mm, 3.74 mm to 3.56 mm, and 5.94 mm to 5.87 mm. Compared to the original CycleFCNs model, the improved model has significantly enhanced the dice similarity coefficient of the brainstem, pituitary gland, and optic nerve (P < 0.001). Conclusions The proposed method significantly improved the registration accuracy for multi-modal medical images in NPC cases. These findings have important clinical implications, as increased registration accuracy can lead to more precise tumor segmentation, optimized treatment planning, and ultimately, improved patient outcomes. |
| format | Article |
| id | doaj-art-c31d7e97476f4289b8ce7aac5d3f6989 |
| institution | DOAJ |
| issn | 1748-717X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-c31d7e97476f4289b8ce7aac5d3f69892025-08-20T02:59:28ZengBMCRadiation Oncology1748-717X2025-02-0120111510.1186/s13014-025-02603-0Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs modelYi Guo0Jun Chen1Lin Lu2Lingna Qiu3Linzhen Lan4Feibao Guo5Jinsheng Hong6Department of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityDepartment of Radiation Therapy, Cancer Center, The First Affiliated Hospital of Fujian Medical UniversityAbstract Background Deformable registration plays an important role in the accurate delineation of tumors. Most of the existing deep learning methods ignored two issues that can lead to inaccurate registration, including the limited field of view in MR scans and the different scanning angles that can exist between multimodal images. The purpose of this study is to improve the registration accuracy between CT and MR for nasopharyngeal carcinoma cases. Methods 269 cases were enrolled in the study, and 188 cases were designated for training, while a separate set of 81 cases was reserved for testing. Each case had a CT volume and a T1-MR volume. The treatment table was removed from their CT images. The CycleFCNs model was used for deformable registration, and two strategies including adaptive mask registration strategy and weight allocation strategy were adopted for training. Dice similarity coefficient, Hausdorff distance, precision, and recall were calculated for normal tissues of CT-MR image pairs, before and after the registration. Three deformable registration methods including RayStation, Elastix, and VoxelMorph were compared with the proposed method. Results The registration results of RayStation and Elastix are essentially consistent. Upon employing the VoxelMorph model and the proposed method for registration, a clear trend of increased dice similarity coefficient and decreased hausdorff distance can be observed. It is noteworthy that for the temporomandibular joint, pituitary, optic nerve, and optic chiasma, the proposed method has improved the average dice similarity coefficient from 0.86 to 0.91, 0.87 to 0.93, 0.85 to 0.89, and 0.77 to 0.83, respectively, as compared to RayStation. Additionally, within the same anatomical structures, the average hausdorff distance has been decreased from 2.98 mm to 2.28 mm, 1.83 mm to 1.53 mm, 3.74 mm to 3.56 mm, and 5.94 mm to 5.87 mm. Compared to the original CycleFCNs model, the improved model has significantly enhanced the dice similarity coefficient of the brainstem, pituitary gland, and optic nerve (P < 0.001). Conclusions The proposed method significantly improved the registration accuracy for multi-modal medical images in NPC cases. These findings have important clinical implications, as increased registration accuracy can lead to more precise tumor segmentation, optimized treatment planning, and ultimately, improved patient outcomes.https://doi.org/10.1186/s13014-025-02603-0Deformable registrationMultimodal imagesDeep learningNasopharyngeal carcinoma |
| spellingShingle | Yi Guo Jun Chen Lin Lu Lingna Qiu Linzhen Lan Feibao Guo Jinsheng Hong Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model Radiation Oncology Deformable registration Multimodal images Deep learning Nasopharyngeal carcinoma |
| title | Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model |
| title_full | Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model |
| title_fullStr | Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model |
| title_full_unstemmed | Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model |
| title_short | Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model |
| title_sort | deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based cyclefcns model |
| topic | Deformable registration Multimodal images Deep learning Nasopharyngeal carcinoma |
| url | https://doi.org/10.1186/s13014-025-02603-0 |
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