Systems and methods for probablistic segmentation in anatomical image processing
Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.
1. A computer-implemented method of performing probabilistic segmentation in anatomical image analysis, the method comprising:
receiving a plurality of images of an anatomical structure;
receiving one or more geometric labels of the anatomical structure;
determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images;
receiving an image of a patient's anatomy;
computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation;
computing at least one statistical confidence value based on the probability distribution;
generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value;
determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and
generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold.
2. The method of claim 1 , further comprising: determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value.
3. The method of claim 1 , further comprising:
generating an interactive user interface or prompt based on the statistical confidence value.
4. The method of claim 1 , further comprising:
predicting a physiological or biomechanical property of the patient's anatomy, based on the statistical confidence value.
5. The method of claim 1 , further comprising:
segmenting the image of the patient's anatomy, based on the segmentation boundary.
6. The method of claim 1 , wherein the one or more geometric labels include annotations of a vessel lumen boundary, centerline, surface, or a combination thereof.
7. The method of claim 1 , further comprising:
determining a distance from a point on a centerline of the anatomical structure to a point on a lumen boundary of the anatomical structure;
mapping a region of the parameterized representation to the determined distance; and
generating the probability distribution, based on the mapping of the region of the parameterized representation of the anatomical structure to the determined distance.
8. The method of claim 1 , further comprising:
generating the segmentation boundary further using surface reconstruction.
9. A system for performing probabilistic segmentation in anatomical image analysis, the system comprising:
at least one server storing instructions for performing probabilistic segmentation in anatomical image analysis; and
at least one processor configured to execute the instructions to perform a method including:
receiving a plurality of images of an anatomical structure;
receiving one or more geometric labels of the anatomical structure;
determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images;
receiving an image of a patient's anatomy;
computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation;
computing at least one statistical confidence value based on the probability distribution;
generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value;
determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and
generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold.
10. The system of claim 9 , further comprising:
determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value.
11. The system of claim 9 , wherein the system is further configured for:
generating an interactive user interface or prompt based on the statistical confidence value.
12. The system of claim 9 , wherein the system is further configured for:
predicting a physiological or biomechanical property of the patient's anatomy, based on the statistical confidence value.
13. The system of claim 9 , wherein the system is further configured for:
segmenting the image of the patient's anatomy, based on the segmentation boundary.
14. The system of claim 9 , wherein the one or more geometric labels include annotations of a vessel lumen boundary, centerline, surface, or a combination thereof.
15. The system of claim 9 , wherein the system is further configured for:
determining a distance from a point on a centerline of the anatomical structure to a point on a lumen boundary of the anatomical structure;
mapping a region of the parameterized representation to the determined distance; and
generating the probability distribution, based on the mapping of the region of the parameterized representation of the anatomical structure to the determined distance.
16. The system of claim 15 , wherein the system is further configured for:
generating a statistical confidence score of the determined distance.
17. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions, that when executed by a computer cause the computer to perform a method of performing probabilistic segmentation in anatomical image analysis, the method comprising:
receiving a plurality of images of an anatomical structure;
receiving one or more geometric labels of the anatomical structure;
determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images;
receiving an image of a patient's anatomy;
computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation;
computing at least one statistical confidence value based on the probability distribution;
generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value;
determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and
generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold.
18. The non-transitory computer readable medium of claim 17 , the method further comprising:
determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value.
19. The non-transitory computer readable medium of claim 17 , the method further comprising:
generating an interactive user interface or prompt based on the statistical confidence value.
20. The non-transitory computer readable medium of claim 17 , the method further comprising:
segmenting the image of the patient's anatomy, based on the segmentation boundary.