Abstract: Vertebrae of the spine in volumetric image are detected using multi-stage detection with trained artificial intelligence. In one embodiment, a trained neural network (116) is employed in a first stage to detect individual vertebra in sagittal images. Two-dimensional bounding boxes around the detected vertebrae are combined to generate a three-dimensional model of the spine. A panoramic image of the spine is generated based on the three-dimensional model to create a straightened view of the spine. The trained neural network is employed in a second stage to detect individual vertebra in the panoramic image. Two-dimensional bounding boxes around the detected vertebrae in the panoramic image are translated to three-dimensional space to create three-dimensional image data with three-dimensional bounding boxes.

Abstract: A method for error correction for segmented three dimensional image data. The method includes receiving segmented three dimensional image data, the segmented three dimensional image data being divided into a plurality of slices; correcting at least one contour of the segmented three dimensional image data on at least one slice according to a command from a user to form a corrected contour; and automatically interpolating a correction represented by the corrected contour to a plurality of slices of the segmented three dimensional image data.

Abstract: A method for error correction for segmented three dimensional image data. The method includes receiving segmented three dimensional image data, the segmented three dimensional image data being divided into a plurality of slices; correcting at least one contour of the segmented three dimensional image data on at least one slice according to a command from a user to form a corrected contour; and automatically interpolating a correction represented by the corrected contour to a plurality of slices of the segmented three dimensional image data.