Abstract: The present disclosure relates to a method and apparatus for smart interpolation of annotations on tomographic images. The method includes receiving 3D images obtained by a CT, MRI or PET system, receiving label with contours of regions-of-interest (ROIs) if label is available for the received 3D images either from automatic segmentation algorithm or manual segmentation, creating an empty label if label is not available, recording user changes on each 2D slices of the label, finding two neighboring 2D slices that the user has edited, running interpolation on the user changes, applying the interpolated changes to each 2D slices not edited by the user, and performing post-processing to output the final 3D segmentation of ROIs.

Abstract: The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation.

Abstract: The present disclosure relates to a method and apparatus for training a convolutional neural network (CNN) that is robust to missing input information.

Abstract: The present disclosure relates to a method and apparatus for automatic detection of anatomical sites from tomographic images.

Abstract: The present disclosure relates to a method and apparatus for automatic detection of anatomical sites from tomographic images.

Abstract: The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation. The method includes: receiving 3D images obtained by a CT system or an MRI system; processing the 3D images to transform to patient coordinate system and have the same spatial resolution and matrix size; building a deep learning framework using CNN models for organ segmentation; doubling the size of training dataset by emulating uncollected training data with mirrored images and their corresponding labels; improving the performance by predicting on original and mirrored images and averaging the output probabilities; and post-processing the output from the deep learning framework to obtain final organ segmentation.