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Inter-and intra-observer variation in delineating regions of interest (ROIs)occurs because of differences in expertise level and preferences of theradiation oncologists. We evaluated the accuracy of a segmentation model usingthe U-Net structure to delineate the prostate, bladder, and rectum in malepelvic CT images. The dataset used for training and testing the model consistedof raw CT scan images of 85 prostate cancer patients. We designed a 2D U-Netmodel to directly learn a mapping function that converts a 2D CT grayscaleimage to its corresponding 2D OAR segmented image. Our network contains blocksof convolution 2D layers with variable kernel sizes, channel number, andactivation functions. On the left side of the U-Net model, we used three 3x3convolutions, each followed by a rectified linear unit (ReLu) (activationfunction), and one max pooling operation. On the right side of the U-Net model,we used a 2x2 transposed convolution and two 3x3 convolution networks followedby a ReLu activation function. The automatic segmentation using the U-Netgenerated an average dice similarity coefficient (DC) and standard deviation(SD) of the following: DC +- SD (0.88 +- 0.12), (0.95 +- 0.04), and (0.92 +-0.06) for the prostate, bladder, and rectum, respectively. Furthermore, themean of average surface Hausdorff distance (ASHD) and SD were 1.2 +- 0.9 mm,1.08 +- 0.8 mm, and 0.8 +- 0.6 mm for the prostate, bladder, and rectum,respectively. Our proposed method, which employs the U-Net structure, is highlyaccurate and reproducible for automated ROI segmentation. This provides afoundation to improve automatic delineation of the boundaries between thetarget and surrounding normal soft tissues on a standard radiation therapyplanning CT scan.

Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning