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Systems and methods for anatomic structure segmentation in image analysis

An anatomical structure and image analysis technology, applied in image analysis, details involving 3D image data, image enhancement, etc., can solve problems such as unintegrated, false label components and holes

Pending Publication Date: 2020-03-24
HEARTFLOW
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, these assumptions may not be incorporated into the CNN such that the predicted labels may have spurious components and holes in the segmented objects

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  • Systems and methods for anatomic structure segmentation in image analysis
  • Systems and methods for anatomic structure segmentation in image analysis
  • Systems and methods for anatomic structure segmentation in image analysis

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Embodiment Construction

[0016] Reference will now be made in detail to the exemplary embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

[0017] As mentioned above, the accuracy of the segmentation boundaries determined by current methods may be limited to image elements, such as pixels or voxels. In these cases, placing segmentation boundaries at voxel locations may introduce errors. In some cases, certain assumptions may not be considered by current predictive models, such as structures of interest not containing pores or disconnected structures. Therefore, there is a need to build models that can predict segmentation boundaries with sub-pixel or sub-voxel accuracy and / or ensure that important assumptions are incorporated into the model.

[0018] The present disclosure is concerned with providing accurate predictions of segmenta...

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Abstract

The present invention discloses systems and methods are disclosed for anatomic structure segmentation in image analysis, using a computer system. One method includes: receiving an annotation and a plurality of keypoints for an anatomic structure in one or more images; computing distances from the plurality of keypoints to a boundary of the anatomic structure; training a model, using data in the one or more images and the computed distances, for predicting a boundary in the anatomic structure in an image of a patient's anatomy; receiving the image of the patient's anatomy including the anatomicstructure; estimating a segmentation boundary in the anatomic structure in the image of the patient's anatomy; and predicting, using the trained model, a boundary location in the anatomic structure in the image of the patient's anatomy by generating a regression of distances from keypoints in the anatomic structure in the image of the patient's anatomy to the estimated boundary.

Description

[0001] related application [0002] This application claims priority to US Provisional Application No. 62 / 503,838, filed May 9, 2017, the disclosure of which is incorporated herein by reference in its entirety. technical field [0003] Various embodiments of the present disclosure relate generally to medical imaging and related methods. In particular, certain embodiments of the present disclosure relate to systems and methods for anatomical structure segmentation in image analysis. Background technique [0004] The problem of dividing an image into parts often occurs in computer vision and medical image analysis. The current approach is to automate this process using a convolutional neural network (CNN) that is trained to predict a class label for each image element such as a pixel or voxel. CNNs typically include multiple convolutional layers that pass an input (eg, an image or a portion of an image) through a set of filters and non-linear activation functions that can be...

Claims

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Application Information

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IPC IPC(8): G06T7/149
CPCG06T2207/20112G06T2207/10072G06T2207/20081G06T2207/30101G06T7/149G06T7/174G06T7/12G06T7/0012G06T2207/30004G06T2207/10088G06T2207/10081G06T2200/04G06T2207/10132G06T2207/10108G06T2207/10104G06V2201/03
Inventor L.格拉迪P.K.彼得森M.沙普D.勒萨热
Owner HEARTFLOW