System and method for automated detection and segmentation of tumor boundaries within medical imaging data
First Claim
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1. A method for classifying regions of interest within a medical image, comprising the steps of:
- training a classifier on a set of medical image training data, which training data includes segmented regions where a clinical ground truth classifying the segmented regions is known;
acquiring non-training medical image data for investigation;
generating an initial segmentation for a region of interest of the medical image;
generating a plurality of candidate segmentations based on the initial segmentation;
comparing the initial and the plurality of candidate segmentations with each other;
selecting a best segmentation from a set of segmentations including the initial and the plurality of candidate segmentations based on the comparisons, where the selected best segmentation is used to train the classifier;
processing the segmented regions to extract a full feature set for each of the segmented regions; and
classifying the regions of interest using the full feature set;
wherein, the step of training includes using a recommender to realize a stable segmentation.
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Abstract
A method for segmenting regions within a medical image includes evaluating a set of candidate segmentations generated from an initial segmentation. Based on distance calculations for each candidate using derivative segmentations, the best candidate is recommended to clinician if it is better than the initial segmentation. This recommender realizes a most stable segmentation that will benefit follow-up computer aided diagnosis (i.e. classifying lesion to benign/malignant).
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Citations
20 Claims
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1. A method for classifying regions of interest within a medical image, comprising the steps of:
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training a classifier on a set of medical image training data, which training data includes segmented regions where a clinical ground truth classifying the segmented regions is known; acquiring non-training medical image data for investigation; generating an initial segmentation for a region of interest of the medical image; generating a plurality of candidate segmentations based on the initial segmentation; comparing the initial and the plurality of candidate segmentations with each other; selecting a best segmentation from a set of segmentations including the initial and the plurality of candidate segmentations based on the comparisons, where the selected best segmentation is used to train the classifier; processing the segmented regions to extract a full feature set for each of the segmented regions; and classifying the regions of interest using the full feature set; wherein, the step of training includes using a recommender to realize a stable segmentation. - View Dependent Claims (2, 3, 7, 8, 9, 10, 11, 12)
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4. A system, comprising:
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a segmenter configured to; segment a region of interest within a medical image to produce an initial segmentation, and generate a plurality of co-existing candidate segmentations for the region of interest by varying or perturbing boundaries of the initial segmentation, wherein if it is determined that one of the plurality of co-existing segmentations is better suited for post segmentation processing than the initial segmentation, then the segmenter recommends changing the initial segmentation, and wherein if it is determined that the initial segmentation is better suited for post segmentation processing than the plurality of co-existing candidate segmentations, then a recommendation to change the initial segmentation is not made. - View Dependent Claims (5, 6)
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13. A method, comprising:
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obtaining an initial segmentation; generating a candidate segmentation set for segmentation of a region of interest in a medical image, where the candidate segmentation set comprises at least a first candidate segmentation and a second candidate segmentation; and evaluating the initial segmentation and individual segmentations of the candidate segmentation set against one another in a non-iterative action to produce an evaluation result; and making a determination of a better segmentation among the initial segmentation and individual candidate segmentations of the candidate segmentation set based, at least in part, on the evaluation result. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification