Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

Abstract

Abstract:The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

Detecting Cancerous Regions in DCE MRI using Functional Data, XGboost and Neural Networks

“…Many examples of machine learning applications on MRI modalities are given in paper [13]. Papers [19] and [1] conducted research tests the possibility of using T2W sequences for cancer localization. Another examples are usage of DWI sequences to solve prostate cancer segmentation and severity evaluation problems presented in papers [26], [18], [29] and [4].…”