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DEEP LEARNING WITH ORTHOGONAL VOLUMETRIC HED SEGMENTATION AND 3D SURFACE RECONSTRUCTION MODEL OF PROSTATE MRI

文献类型： 会议论文

第一作者： Ruida Cheng

作者： Ruida Cheng 1 ; Nathan Lay 2 ; Francesca Mertan 3 ; Baris Turkbey 3 ; Holger R. Roth 2 ; Le Lu 2 ; William Gandler 1 ; Evan S. McCreedy 1 ; Thomas Pohida 4 ; Peter Choyke 3 ; Matthew J. McAuliffe 1 ; Ronald M. Summers 2 ;

作者机构： 1.Imaging Sciences Laboratory, Center for Information Technology

2.Imaging Biomarkers and CAD Laboratory, Clinical Center

3.Molecular Imaging Program

4.Computational Bioscience and Engineering Laboratory, Center for Information Technology

关键词： Prostate segmentation;HED;Deep learning

会议名称： IEEE International Symposium on Biomedical Imaging

主办单位：

页码： 749-753

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%.

分类号： TP391.41-53

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