Features, such as anatomical features, may be automatically segmented from medical imaging information, using a computer-implemented method. In an example, three-dimensional (3D) medical imaging information may be received, such as defining a first volume. A first trained convolutional neural network (CNN) may be applied to the three-dimensional medical imaging information. An output from the first trained CNN may be used to determine a region-of-interest within the first volume, the region-of-interest defining a lesser, second volume. A different, second trained CNN may be applied to the region-of-interest, a segmented representation of the 3D medical imaging information may be provided using the outputs from the first and second CNNs, where the second CNN provides enhanced segmentation detail in the region-of-interest without requiring application of the second CNN to an entirety of the first volume. Techniques are also described from training one or more of the first and second CNNs.
Automated image segmentation using DCNN such as for radiation therapy