Probablistic segmentation
AssigneeRaySearch Laboratories AB
InventorsMarcus Nordstrom, Jonas Soderberg, David Lidberg
Life of this patent
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Abstract
A machine learning system may be used for determining if a segmentation of a medical image is a reasonable segmentation in the sense that it is a segmentation that could be made by a human user and does not contain any impossible combinations of pixel values. The method is enhanced by user input to avoid the impossible combinations.
Claims9
A computer-implemented method of determining an improved segmentation of a medical image, comprising:
- providing the medical image to a neural network based machine learning system that has been trained by means of training data sets comprising manually made segmentations, to determine if a segmentation is similar to the segmentations in the training dataset, wherein the medical image includes a set X of pixels or voxels having given pixel values x to a corresponding set of Y of segmentation values y;
- obtaining an initial segmentation of the medical image from the machine learning system in the form of a set Y of segmentation values y, said segmentation values y indicating for each pixel if it belongs to the structure, wherein the machine learning system approximates the unnormalized conditional probability distribution with a neural network;
- manually adjusting the initial segmentation by changing at least one of the segmentation values y to y1 and produce an adjusted set Y′ of segmentation values y′=[y1,y2];
- and updating, by the machine learning system, the initial segmentation to produce an updated segmentation by optimizing the set Y of segmentation values based on the given pixel values x and the adjusted set Y′ of segmentation values, by solving the optimization problem.
A computer program product for generating an improved segmentation of a medical image, the computer program product comprising computer readable code means which, when run in a computer will cause the computer to perform the following steps:
- receiving the medical image at a neural network based machine learning system that has been trained by means of training data sets comprising manually made segmentations, to determine if a segmentation is similar to the segmentations in the training dataset, wherein the medical image includes a set X of pixels or voxels having given pixel values x to a corresponding set of Y of segmentation values y;
- obtaining an initial segmentation of a structure in the image in the form of a set Y of segmentation values y, said segmentation values y indicating for each pixel if it belongs to the structure, wherein the machine learning system approximates the unnormalized conditional probability distribution with a neural network;
- receiving manual input for adjusting the initial segmentation by changing at least one of the segmentation values y to y1 and produce an adjusted set Y′ of segmentation values y′=[y1,y2] based on the manual input;
- updating, by the machine learning system the initial segmentation to produce an updated segmentation by optimizing the set Y of segmentation values based on the given pixel values x and the adjusted set Y′ of segmentation values by solving the optimization problem;
- and optionally repeating the receiving and updating, using the updated segmentation as the initial segmentation, until the updated segmentation is found to be good enough.
Dependencies are inferred from the claim text.