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Harnessing clinical annotations to improve deep learning performance in prostate segmentation

Fig 1

3D U-Net model diagram and preprocessing steps.

A) Network diagram of the 3D U-Net used for this study. Numbers within the ovals represent number of feature maps at that layer. Connections represent network operations, such as 3x3x3 3D convolution (“Conv”), 2x2x2 max pooling (“Max Pool”), 3x3x3 3D transposed convolution (“Deconv”), skip feature map concatenation (“Concat”), batch normalization (“BN”), rectified linear unit activation (“ReLU”), and softmax output (“Softmax”). B) Process diagram of preprocessing steps. Once images were imported from the archive (either PACS or challenge download), N4ITK bias field correction was applied. Images were then resampled to 1mm isotropic resolution and IQR normalized. During training, real-time augmentation was applied to each input image to create the training sample for that epoch.

Fig 1