Automatic method and system for vessel refine segmentation in biomedical images using tree structure based deep learning model
First Claim
1. A system for analyzing a biomedical image including at least one tree structure object, comprising:
- a communication interface configured to receive a learning model and a plurality of model inputs derived from the biomedical image, the biomedical image being acquired by an image acquisition device; and
at least one processor, configured to apply the learning model to the plurality of model inputs to analyze the biomedical image, wherein the learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively, wherein the second network is a tree structure network that models a spatial constraint of the tree structure object.
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Abstract
Embodiments of the disclosure provide systems and methods for analyzing a biomedical image including at least one tree structure object. The system includes a communication interface configured to receive a learning model and a plurality of model inputs derived from the biomedical image. The biomedical image is acquired by an image acquisition device. The system further includes at least one processor configured to apply the learning model to the plurality of model inputs to analyze the biomedical image. The learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively. The second network is a tree structure network that models a spatial constraint of the tree structure object.
11 Citations
20 Claims
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1. A system for analyzing a biomedical image including at least one tree structure object, comprising:
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a communication interface configured to receive a learning model and a plurality of model inputs derived from the biomedical image, the biomedical image being acquired by an image acquisition device; and at least one processor, configured to apply the learning model to the plurality of model inputs to analyze the biomedical image, wherein the learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively, wherein the second network is a tree structure network that models a spatial constraint of the tree structure object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for analyzing a biomedical image including at least one tree structure object, comprising:
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receiving a learning model and a plurality of model inputs derived from the biomedical image, the biomedical image being acquired by an image acquisition device; and applying, by at least one processor, the learning model to the plurality of model inputs to analyze the biomedical image, wherein the learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively, wherein the second network is a tree structure network that models a spatial constraint of the tree structure object. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A non-transitory computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by at least one processor, performs a method for analyzing a biomedical image including at least one tree structure object, the method comprising:
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receiving a learning model and a plurality of model inputs derived from the biomedical image, the biomedical image being acquired by an image acquisition device; and applying the learning model to the plurality of model inputs to analyze the biomedical image, wherein the learning model includes a first network configured to process the plurality of model inputs to construct respective feature maps and a second network configured to process the feature maps collectively, wherein the second network is a tree structure network that models a spatial constraint of the tree structure object.
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Specification