A Comprehensive Segmentation Registration and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE MRI

摘要

Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising technique for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion, as done by schemes such as the 3 time point (TP) scheme. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in faithfully embedding high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. In this paper, we present a novel unsupervised detection scheme for CaP from 3T DCE-MRI that combines LLE and consensus clustering to discriminate between tissue classes at the image pixel level. The methodology comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from in vivo 3 T MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. LLE followed by consensus clustering is finally used to identify distinct clusters. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth yielded a maximum CaP detection sensitivity of 60.72% and specificity of 83.24% while the popular 3TP scheme gave an accuracy of 38.22%.