Deep Learning With Orthogonal Volumetric Hed Segmentation And 3d Surface Reconstruction Model Of Prostate Mri
2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017)(2017)
摘要
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%.
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关键词
Prostate segmentation, HED, Deep learning
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