A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients
Abstract Background Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-06-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12880-019-0348-y |
| _version_ | 1818696075808079872 |
|---|---|
| author | Ranran Sun Keqiang Wang Lu Guo Chengwen Yang Jie Chen Yalin Ti Yu Sa |
| author_facet | Ranran Sun Keqiang Wang Lu Guo Chengwen Yang Jie Chen Yalin Ti Yu Sa |
| author_sort | Ranran Sun |
| collection | DOAJ |
| description | Abstract Background Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency. Methods Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice’s similarity coefficient (DSC) and Sensitivity and Specificity. Results Experimental study with the five patients’ data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets. Conclusions Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy. |
| first_indexed | 2024-12-17T13:55:36Z |
| format | Article |
| id | doaj.art-362dd41876bc4ae28882b525a0730403 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-17T13:55:36Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-362dd41876bc4ae28882b525a07304032022-12-21T21:45:56ZengBMCBMC Medical Imaging1471-23422019-06-011911910.1186/s12880-019-0348-yA potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patientsRanran Sun0Keqiang Wang1Lu Guo2Chengwen Yang3Jie Chen4Yalin Ti5Yu Sa6Department of Biomedical Engineering, Tianjin UniversityDepartment of Biomedical Engineering, Tianjin UniversityDepartment of Biomedical Engineering, Tianjin UniversityDepartment of Biomedical Engineering, Tianjin UniversityDepartment of Radiation Oncology, Tianjin Cancer HospitalGlobal Research Organization, GE HealthcareDepartment of Biomedical Engineering, Tianjin UniversityAbstract Background Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency. Methods Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice’s similarity coefficient (DSC) and Sensitivity and Specificity. Results Experimental study with the five patients’ data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets. Conclusions Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.http://link.springer.com/article/10.1186/s12880-019-0348-yBrain tumorFunctional magnetic resonance imagingFusionSemi-automatic segmentation |
| spellingShingle | Ranran Sun Keqiang Wang Lu Guo Chengwen Yang Jie Chen Yalin Ti Yu Sa A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients BMC Medical Imaging Brain tumor Functional magnetic resonance imaging Fusion Semi-automatic segmentation |
| title | A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients |
| title_full | A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients |
| title_fullStr | A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients |
| title_full_unstemmed | A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients |
| title_short | A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients |
| title_sort | potential field segmentation based method for tumor segmentation on multi parametric mri of glioma cancer patients |
| topic | Brain tumor Functional magnetic resonance imaging Fusion Semi-automatic segmentation |
| url | http://link.springer.com/article/10.1186/s12880-019-0348-y |
| work_keys_str_mv | AT ranransun apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT keqiangwang apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT luguo apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT chengwenyang apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT jiechen apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT yalinti apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT yusa apotentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT ranransun potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT keqiangwang potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT luguo potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT chengwenyang potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT jiechen potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT yalinti potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients AT yusa potentialfieldsegmentationbasedmethodfortumorsegmentationonmultiparametricmriofgliomacancerpatients |