Purpose: Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI).
Methods: In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer.
Results: The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42.
Conclusions: The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.