Prostate cancer (PCa) is the most common cancer among men with 174,650 new cases and 31,650 deaths in the US expected in 2019. Prostate MRI was recently established as a good modality for diagnosing clinically significant cancer (csPCa). Automatic segmentation of the prostate is an important part of current research in computer aided diagnosis (CAD) and optimized acquisition of MRI. State-of-the art segmentation uses standard convolution neural networks (CNN) using only axial t2-weighted MRI. The anisotropy caused by the much lower inter-plane MRI resolution results in segmentation errors in the apex and base region. Meyer et al. recently showed that adding the sagittal and coronal t2-weighted series to a conventional isotropic CNN improves segmentation performance. We propose an MRI specific CNN that is designed to handle anisotropic, multi-planar acquisitions and reduce annotation and computational effort by extending our previously developed anisotropic single plane CNN
Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation
T. Riepe, M. Hosseinzadeh, P. Brand, and H. Huisman
Annual Meeting of the International Society for Magnetic Resonance in Medicine 2020.
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