received by the International Bureau on 5 June 2017 (05.06.2017)

What is claimed is:

1 . A method for automated segmentation of lesions within a region of interest of a patient, comprising:

producing at least one magnetic resonance imaging (MRI) image of the region of interest;

generating at least one probability map from the at least one MRI image, a given probability map representing, for each pixel, a likelihood that a lesion is present at the location represented by the pixel given the at least one MRI image of the region of interest, the at least one probability map including a cumulative probability map representing longitudinally acquired MRI image data for the patient, such that each of a plurality of pixels comprising the cumulative probability map is determined from a set of probability values at that location across a plurality of probability maps from previous MRI images of the patient;

combining the at least one probability map with a plurality of additional probability maps, each representing the likelihood that a lesion is present at each position within the map, to provide a composite probability map; and

identifying lesions from the composite probability map.

2. The method of claim 1 , the plurality of additional probability maps comprising a first probability map representing, for each pixel, a likelihood that a lesion is present at the location represented by the pixel given previous data sets from a population of patients, the population of patients including patients other than the patient; and

3. The method of claim 2, wherein the first probability map is a generic MS lesion probability map combines lesion maps across the population of patients to represent the regions in the brain for which it is most likely that lesions will form.

4. The method of claim 2, wherein the first probability map is a false positive probability map combining maps of false positive results across the population of patients representing regions having a high likelihood of being false positives across patients.

5. The method of claim 2, wherein the first probability map is a generic anatomic probability map of gray matter.

6. The method of claim 1 , wherein combining the at least one probability map with a plurality of additional probability maps comprises combining the at least one probability map with a plurality of additional probability maps such that each pixel of the composite map is a weighted linear combination of the corresponding pixels from the at least one probability map and the plurality of additional probability maps.

7. The method of claim 6, wherein a set of weights for the weighted linear combination is determined using an expert system trained on prior datasets.

8. The method of claim 6, wherein the set of weights for the weighted linear combination are different across each of a plurality of regions comprising the image, and the weights for each region are determined via a regression analysis on prior datasets.

9. A system for automated segmentation of lesions within a region of interest of a patient comprising:

a magnetic resonance imaging (MRI) interface that receives at least one MRI image of the region of interest;

a probability map generator that generates at least one probability map, a given probability map representing, for each pixel, a likelihood that a lesion is present at the

17 location represented by the pixel given the at least one MRI image of the region of interest; and

a probability reconciliation component that combines the at least one probability map with a generic MS lesion probability map combining lesion maps across a large population of patients to represent the regions in the brain for which it is most likely that lesions will form and a false positive probability map combining maps of false positive results across the population of patients representing regions having a high likelihood of being false positives across patients to provide a composite probability map.

10. The system of claim 9, wherein the at least one probability map includes a binary map representing a classification of each pixel in the region of interest from intensity values of the at least one MR image and a cumulative probability map representing longitudinally acquired MRI image data for the patient, such that each of a plurality of pixels comprising the cumulative probability map is determined from a set of probability values at that location across a plurality of probability maps from previous MRI images.

1 1. The system of claim 10, wherein the probability reconciliation component combines the binary map, the cumulative probability map, the generic MS lesion probability map, the false positive probability map, and a generic anatomic probability map of gray matter to provide the composite image.

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