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Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast
Stefanie J C G Hectors1, Igor Jacobs, Gustav J Strijkers
1Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands, s.j.c.g.hectors@tue.nl.
Object:
Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only.
Materials And Methods:
Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes.
Results:
The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96).
Conclusion:
Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.
Insights
This study demonstrates that automatic tumor segmentation is feasible using only intrinsic MRI contrast. Multiparametric cluster analysis of T2 and apparent diffusion coefficient (ADC) mapping accurately segments mouse tumors without contrast agents.
Area of Science:
- Biomedical Imaging
- Radiology
- Computational Biology
Background:
- Contrast-enhanced T1-weighted imaging is standard for MRI tumor segmentation.
- MR contrast agents are not suitable for all applications.
- Intrinsic MRI contrast offers an alternative for segmentation.
Purpose of the Study:
- To assess the feasibility of automatic tumor segmentation using only intrinsic MRI contrast.
- To evaluate multiparametric cluster analysis for this purpose.
Main Methods:
- Multiparametric MRI (T1, T2, ADC mapping) was performed on 21 mouse tumor models.
- k-means and fuzzy c-means clustering were applied to MRI parameters.
- Segmentation accuracy was validated against manual and histology-derived volumes.
Main Results:
- K-means clustering using {T2, ADC} parameters achieved the highest similarity with manual segmentation (SI=0.82).
- Strong linear correlations were found between automatic, manual, and histology-derived tumor volumes (R²=0.99 and R²=0.96, respectively).
Conclusions:
- Automatic tumor segmentation is achievable using only intrinsic MRI contrast.
- Multiparametric cluster analysis provides a viable alternative to contrast-enhanced imaging.
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