Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

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 (R² = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R² = 0.96). Conclusion: Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.