Title

Automatic segmentation of the prostate gland on planning ct images using deep neural networks (DNN).

Document Type

Article

Publication Date

2018

Publication Title

Med Phys

Abstract

Purpose: Recent advances in deep neural networks (DNN) have opened the doors toward application of DNN for automatic image segmentation. We have evaluated a DNN-based algorithm for automatic segmentation of the prostate gland on a large cohort of patient images. Methods: Planning-CT (pCT) datasets for 1114 prostate cancer patients were retrospectively selected and divided into 2 groups. Group A contained 1104 datasets, with prostate gland contours delineated by physician on each dataset. Among these image sets, 964 were used for training/validation and 140 were used for testing. Group B contained 10 datasets; each including prostate contours delineated by 5 independent physicians, and consensus contour generated with the STAPLE method in CERR to combine the 5 physician contours. All images were resampled to spatial resolution of 1 mm × 1 mm × 1.5 mm. A region (128 × 128 × 64) was selected in the vicinity of the prostate to train a DNN. The top performing DNN was chosen based on validation results, and used to segment the prostate on all testing images. Results were compared between DNN and physician-generated contours using Dice coefficient (DSC). Results: Mean DSC between automatic DNN-based prostate segmentation and physician-delineated contours for test data in group A was 0.85 + 0.06 [range = 0.65, 0.93]. Mean DSC between automatic DNN-based prostate segmentation and the 5 physician contours in group B was 0.85 + 0.04 [range = 0.80, 0.91]. Mean DSC between automatic DNN-based prostate segmentation and the consensus contour in group B was 0.88 + 0.03 [range = 0.82, 0.92]. Conclusion: A deep-neural-network-based algorithm was used to automatically segment the prostate for a large cohort of prostate cancer patients. Furthermore, the DNN prostate segmentations were compared to consensus contour for a smaller group of patients; the agreement between DDN segmentations and consensus contour was similar to the agreement reported in a previous study between clinician contours and consensus. Clinical use of DNN is promising, but further investigation is warranted.

Volume

45

Issue

6

First Page

e464

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