A method of analysing data from an angiographic scan that provides three-dimensional information about blood vessels in a patient's brain, the method comprising the steps of: processing the data (26) to produce a three-dimensional image; extracting the system of blood vessels inside the skull, so as to obtain a vessel mask (28); skeletonising (30) the vessel mask with a thinning algorithm to produce a skeleton mask performing a central plane extraction; analysing (32) the skeleton mask to identify voxels that have more than two neighbours, indicating a fork, bifurcation or branch; detecting the most proximal location of each of the three main supplying arteries of the head in the skeleton mask to identify starting positions; and then starting from each starting position in turn, and walking along the line representing the corresponding blood vessel to detect (34) a plurality of anatomical markers within the network of blood vessels.

A method of analysing data from an angiographic scan that provides three-dimensional information about blood vessels in a patient's brain, the method comprising the steps of: processing the data (26) to produce a three-dimensional image; extracting the system of blood vessels inside the skull, so as to obtain a vessel mask (28); skeletonising (30) the vessel mask with a thinning algorithm to produce a skeleton mask performing a central plane extraction; analysing (32) the skeleton mask to identify voxels that have more than two neighbours, indicating a fork, bifurcation or branch; detecting the most proximal location of each of the three main supplying arteries of the head in the skeleton mask to identify starting positions; and then starting from each starting position in turn, and walking along the line representing the corresponding blood vessel to detect (34) a plurality of anatomical markers within the network of blood vessels.

The present disclosure relates to a computer-implemented method for completing an image, the method comprising the steps of dividing data of an image to be completed into a plurality of image portions. The method entails applying a first filling process to fill a first image portion comprising a first hole, the first hole associated with a first quantity and/or a first quality; and applying a second filling process to fill a second image portion comprising a second hole, the second hole associated with a second quantity different to the first quantity and/or a second quality different to the first quality, the second process being different to first process. The method then includes combining the filled first and second image portions to complete the image.

The present disclosure relates to a computer-implemented method for completing an image, the method comprising the steps of dividing data of an image to be completed into a plurality of image portions. The method entails applying a first filling process to fill a first image portion comprising a first hole, the first hole associated with a first quantity and/or a first quality; and applying a second filling process to fill a second image portion comprising a second hole, the second hole associated with a second quantity different to the first quantity and/or a second quality different to the first quality, the second process being different to first process. The method then includes combining the filled first and second image portions to complete the image.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that upsample and refine segmentation masks. Indeed, in one or more implementations, a segmentation mask refinement and upsampling system upsamples a preliminary segmentation mask utilizing a patch-based refinement process to generate a patch-based refined segmentation mask. The segmentation mask refinement and upsampling system then fuses the patch-based refined segmentation mask with an upsampled version of the preliminary segmentation mask. By fusing the patch-based refined segmentation mask with the upsampled preliminary segmentation mask, the segmentation mask refinement and upsampling system maintains a global perspective and helps avoid artifacts due to the local patch-based refinement process.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that upsample and refine segmentation masks. Indeed, in one or more implementations, a segmentation mask refinement and upsampling system upsamples a preliminary segmentation mask utilizing a patch-based refinement process to generate a patch-based refined segmentation mask. The segmentation mask refinement and upsampling system then fuses the patch-based refined segmentation mask with an upsampled version of the preliminary segmentation mask. By fusing the patch-based refined segmentation mask with the upsampled preliminary segmentation mask, the segmentation mask refinement and upsampling system maintains a global perspective and helps avoid artifacts due to the local patch-based refinement process.

A method for analyzing scan data. In some embodiments, the method includes forming, from a first scan data array, a first mask, each element of the first mask being one or zero according to whether the corresponding element of the first scan data array exceeds a first threshold; forming, from the first scan data array, a second mask, each element of the second mask having a value of one or zero according to whether the corresponding element of the first scan data array exceeds a second threshold, the second threshold being less than the first threshold; and forming a fourth mask, the fourth mask being the element-wise product of the second mask and a third mask, the third mask being based on the first mask.

A method for analyzing scan data. In some embodiments, the method includes forming, from a first scan data array, a first mask, each element of the first mask being one or zero according to whether the corresponding element of the first scan data array exceeds a first threshold; forming, from the first scan data array, a second mask, each element of the second mask having a value of one or zero according to whether the corresponding element of the first scan data array exceeds a second threshold, the second threshold being less than the first threshold; and forming a fourth mask, the fourth mask being the element-wise product of the second mask and a third mask, the third mask being based on the first mask.

Various embodiments relate to a computer apparatus for the bone microstructure connectivity recovery of a skeletal image reconstructed through an artificial neural network using the representations of a node-link graph-based bone microstructure and a method thereof. The computer apparatus and the method may be configured to represent a node-link graph from a bone microstructure of an input skeletal image, reinforce a connectivity of the bone microstructure in the node-link graph, and change the node-link graph into a skeletal image.

The present disclosure relates to an information processing apparatus, an information processing method, and a program capable of performing a process for a desired depth value range of depth data.

A maximum value and a minimum value of depth values of depth data corresponding to image data are set to thereby set a depth value range from the minimum value to the maximum value. A mask for the depth data is generated according to the depth value range thus set. For example, the present disclosure is applicable to an information processing apparatus, electronic equipment, an information processing method, a program, or the like.