Sequential learning technique for medical image segmentation

A medical image segmentation method for locating an anatomical structure in an image of interest to a user, using a training set of one or more anatomical images, the method comprising:

• (a) decomposing each anatomical image in the training set into a plurality of regions;
• (b) generating, for each anatomical image in the training set, a hierarchy that includes a plurality of levels, each level in the hierarchy corresponding to one of the plurality of regions;
• (c) training, for each anatomical image in the training set, a plurality of first classifiers to estimate one or more annotations, and storing the estimated one or more annotations by the first classifiers as previously derived annotations;
• (d) training, via sequential training, a plurality of second classifiers to approximate the previously derived annotations, where the second classifiers are trained in a particular sequence such that the output of one of the second classifiers becomes input to another one of the second classifiers, and wherein the sequential training of the second classifiers is based on an approximation technique derived from a Bayesian approximation of a probabilistic distribution of annotations;
• (e) storing the trained, second classifiers in (d);
• and
• (f) locating an anatomical structure in the image of interest to the user based on stored trained, second classifiers.

An article of manufacture comprising a non-transitory computer storage medium storing computer readable program code which, when executed by a computer, implements a computer-based medical image segmentation method for locating an anatomical structure in an image of interest to a user, using a training set of one or more annotated, anatomical images, the method comprising:

• (a) computer readable program code decomposing each annotated, anatomical image in the training set into a plurality of regions;
• (b) computer readable program code generating, for each annotated, anatomical image in the training set, a hierarchy that includes a plurality of levels, each level in the hierarchy corresponding to one region of the plurality of regions;
• (c) computer readable program code obtaining, from storage, at least one previously derived annotation for each level in the hierarchy and training, via sequential training, a plurality of first classifiers to approximate the at least one previously derived annotation, wherein the first classifiers are trained in a particular sequence such that the output of one of the first classifiers becomes input to another one of the first classifiers, the at least one previously derived annotation estimated by a plurality of second classifiers, and wherein the sequential training of the first classifiers is based on an approximation technique derived from a Bayesian approximation of a probabilistic distribution of annotations;
• (d) computer readable program code storing the trained, first classifiers of (c);
• and
• (e) computer readable program code locating an anatomical structure in the image of interest to the user based on stored trained, first classifiers.

An article of manufacture comprising non-transitory computer storage medium storing computer readable program code which, when executed by a computer, implements a computer-based medical image segmentation method for locating an anatomical structure in an image of interest to a user, using a training set of one or more anatomical images, the method comprising:

• (a) computer readable program code decomposing each anatomical image in the training set into a plurality of regions;
• (b) computer readable program code generating, for each anatomical image in the training set, a hierarchy that includes a plurality of levels, each level in the hierarchy corresponding to one of the plurality of regions;
• (c) computer readable program code training, for each anatomical image in the training set, a plurality of first classifiers to estimate one or more annotations, and storing the estimated one or more annotations by the first classifiers as previously derived annotations;
• (d) computer readable program code training, via sequential training, a plurality of second classifiers to approximate the previously derived annotations, where the second classifiers are trained in a particular sequence such that the output of one of the second classifiers becomes an input to another one of the second classifiers, and wherein the sequential training of the second classifiers is based on an approximation technique derived from a Bayesian approximation of a probabilistic distribution of annotations;
• (e) computer readable program code outputting trained, second classifiers in (d);
• and
• (f) computer readable program code locating an anatomical structure in the image of interest to the user based on stored trained, second classifiers.