Deep learning with orthogonal volumetric HED segmentation and 3D surface reconstruction model of prostate MRI

Abstract

Automatic MR whole prostate segmentation is a challenging task. Recent approaches have attempted to harness the capabilities of deep learning for MR prostate segmentation to tackle pixel-level labeling tasks. Patch-based and hierarchical features-based deep CNN models were used to delineate the prostate boundary. To further investigate this problem, we introduce a Holistically-Nested Edge Detector (HED) MRI prostate deep learning segmentation and 3D surface reconstruction model that facilitate the registration of multi-parametric MRI with histopathology slides from radical prostatectomy specimens and targeted biopsy specimens. Application of this technique combines deep learning and computer aided design to provide a generalized solution to construct a high-resolution 3D prostate surface from MRI images in three orthogonal views. The performance of the segmentation is evaluated with MRI scans of 100 patients in 4-fold cross-validation. We achieve a mean Dice Similarity of 88.6%.