Patent ID: 11915464
Assignee: CONTEXTFLOW GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A system for creating a medical image database, comprising a training unit and an indexing unit downstream of the training unit,
wherein the training unit is designed,
under a specification of data records in the medical image database, which data records comprise a plurality of partial images of two-dimensional or higher-dimensional initial images of parts of the human body, wherein each of the partial images is a section or block within the initial image of the respective part of the human body,
to create a projection for obtaining feature vectors from the partial images, wherein:
the projection maps partial images from the plurality of partial images that are similar to one another to feature vectors with a short distance, and,

for preparation of the execution of the projection, to create a neural network, based on specified learning partial images from the plurality of partial images, wherein the data records or part of the data records are/is used by the neural network within the scope of a metric learning method to learn the projection and creation of the feature vectors from learning partial images from the specified learning partial images and a specified similarity, between the learning partial images, wherein the metric learning method is based on:
specification of n-tuples of sub-regions originating from a same initial image as the learning partial images, wherein the similarity to be achieved between the learning partial images in question of the n-tuple is dependent on the spatial distance of the relevant sub-regions in the initial image, wherein the learning partial images are considered to be all the more similar, the closer the sub-regions in question are arranged to one another in the initial image,, wherein the indexing unit is designed
to apply the projection, created by the training unit, to the partial images of the data records or to a number of further partial images of further data records, and accordingly to obtain at least one feature vector for each of the partial images or further partial images, and
to store the obtained at least one feature vectors in an index data structure of the medical image database;, wherein a position of each of the partial images of the initial image based on the human body is determined,
and wherein information regarding the position of the partial images is used by a neural network to learn a projection for estimating the positions of partial images,
wherein the projection is learned with a target function that, by mapping pairs or groups of partial images from the plurality of partial images, a spatial constellation of the pairs/groups before and after the projection is similar or
wherein the projection is learned based on a known mapping of the partial images to positions
wherein, in addition to the feature vectors, the information regarding the position of the partial images is stored in the database

wherein, in the presence of a search request, a searched spatial position in the body is determined and the database is searched for feature vectors of partial images for which a same spatial position is stored in their data records or the spatial position of which does not exceed a threshold value, specified by a user, for a spatial distance from the searched position.