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Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN) by Chang Liu; Stephen J. Gardner; Ning Wen; Mohamed A. Elshaikh; Farzan Siddiqui; Benjamin Movsas; Indrin J. Chetty is a Medicine article available to read on EtoBox.

What is Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN) about?

## Purpose: Recent advances in deep neural networks (dnns) have unlocked opportunities for their application 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 and materials: Planning-ct data sets for 1114 patients with prostate cancer were retrospectively selected and divided into 2 groups. group a contained 1104 data sets, with 1 physician-generated prostate gland contour for each data set. among these image sets, 771 were used for training, 193 for validation, and 140 for testing. group b contained 10 data sets, each including prostate contours delineated by 5 independent physicians and a consensus contour generated using the staple method in the cerr software package. all images were resampled to a spatial resolution of 1 × 1 × 1.5 mm. a region (128 × 128 × 64 voxels) containing the prostate was selected to train a dnn. the best-performing model on the validation data sets was used to segment the prostate on all testing images. results were compared between dnn and physician-generated contours using the dice similarity coefficient, hausdorff distances, regiona

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Author

Chang Liu; Stephen J. Gardner; Ning Wen; Mohamed A. Elshaikh; Farzan Siddiqui; Benjamin Movsas; Indrin J. Chetty

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0360-3016)

Published

2019

Language

EN

Field

Medicine (Health Sciences)