Assessing the relevance of multi-planar MRI acquisitions for prostate segmentation using deep learning techniques

Abstract

Background. Prostate segmentation is a crucial step in computer-aided systems for prostate cancer detection. Multi-planar acquisitions are commonly used by clinicians to obtain a more accurate patient diagnosis but their relevance in prostate segmentation using fully automated algorithms has not been assessed. To date, the limited assessment of this relevance stems from the fact that both axial and sagittal prostate imaging views, as opposed to a single view, doubles the acquisition time. In this work, we assess the relevance of multi-planar imaging for prostate segmentation within a deep learning segmentation framework. Materials and Methods. We propose a deep learning prostate segmentation framework either from either axial or from axial and sagittal T2-weighted magnetic resonance images (MRI). The system is based on an ensemble of convolutional neural networks, each independently trained on a single imaging view. We compare single-view (axial) segmentations to those obtained from two imaging views (axial and sagittal) to assess the relevance of using multi-planar acquisitions. Algorithm performance assessment will be two-fold: 1) the global DICE score between the algorithm’s predictions and the segmentations of an experienced reader will be computed and 2) the number of lesions located within the algorithm’s segmentation prediction will be calculated. A subset of 80 patients from the public PROSTATEx-2 database containing both axial and sagittal T2-weighted MRIs will be used for this study. Results. The multiplanar network outperformed the network trained on only axial views according to both the proposed metrics. A statistically significant increase of 4% in DICE scores was found along with an 9% increase in the number of lesions within the predicted segmentation. Conclusions. The proposed method allows for a fully automatic segmentation of the prostate from single- or multi-view MRI and assesses the relevance of multi-planar MRI acquisitions for fully automatic prostate segmentation algorithms.