A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

There is provided a method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of the image, a depth value, performing a local background enclosure calculation on the input image to determine salient object information, and generating a visual stimulus to visualise the salient object information using the visual stimulation device. Determining the salient object information is based on a spatial variance of at least one of the multiple points of the image in relation to a surface model that defines a surface in the input image.

There is provided a method for creating artificial vision with an implantable visual stimulation device. The method comprises receiving image data comprising, for each of multiple points of the image, a depth value, performing a local background enclosure calculation on the input image to determine salient object information, and generating a visual stimulus to visualise the salient object information using the visual stimulation device. Determining the salient object information is based on a spatial variance of at least one of the multiple points of the image in relation to a surface model that defines a surface in the input image.