SU‐GG‐I‐107: Unsupervised Nonlinear Dimensionality Reduction for Multiparametric Oncological Image Segmentation

Purpose: To investigate the use of unsupervised non‐linear dimensionality reduction (NLDR) techniques for segmentation and quantification of multi‐parametric breast MRI. Methods and Materials: Five patients underwent breast MRI after suspicious findings on mammograms or clinical breast exams. All patients underwent either biopsy or mastectomy and histopathological analysis was performed. MR sequences were T1‐weighted (T1WI:TR/TE=13/4.6ms), T2‐weighted (T2WI:TR/TE=5700/102, FOV=18×18cm,256×192,ST=4mm), Dynamic Contrast‐Enhanced (DCE:TR/TE=20/40, FOV=18×18cm,) after injection of GdDTPA contrast agent and Diffusion Weighted Imaging (DWI:TR/TE=5000/90ms,128×128,b=0,500, 750,1000s/mm²). MR data was co‐registered before application of the NDLR methods using a non‐deformable model. NLDR methods used were Isomap, Fuzzy Isomap, Locally Linear Embedding (LLE) and Diffusion‐Maps. The embedded image was constructed by projecting the feature spaces (image intensities) associated with each of the MR sequences into a one dimensional embedding space (manifold). The segmented regions were defined and lesion contours (LCs) were then detected by both embedded and DCE‐MR. DCE‐MR was selected for the ground truth for malignant breast lesions. Dice similarity (DS) indices were calculated and statistical analysis was performed on them. Results: All patients had invasive ductal carcinoma. The segmented regions were congruent with a median DS of (Isomap:86.47%±17.6%), (Fuzzy Isomap:86.48%±15.3%), (Diffusion‐Maps: 86.44%±15.9%) and (LEE:91.51%±4.3%). The computational load varied with the LLE as the having lowest compared to Isomap and Diffusion‐Maps. Interesting, the LEE was able to efficiently segment breast normal and abnormal tissue using only T1/T2 and DCE. However by including DWI with multiple b‐values, isomap showed better performance but with increased computationally costs. Conclusion: There was high similarity between multi‐parametric embedded and DCE‐MR images using NLDR methods. Using unsupervised NDLR, we demonstrated an improvement in the accuracy of the lesion segmentation and performed better than a single parameter.