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Systems and methods for segmenting medical images based on anatomical landmark-based features

A medical image and anatomy technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problem that the 3DCT image is not the most ideal.

Active Publication Date: 2017-08-18
ELEKTA AB
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  • Abstract
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  • Claims
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Problems solved by technology

Therefore, these segmentation methods are useful for such as figure 1 The lower quality of the 3D CT image shown is still suboptimal

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  • Systems and methods for segmenting medical images based on anatomical landmark-based features
  • Systems and methods for segmenting medical images based on anatomical landmark-based features
  • Systems and methods for segmenting medical images based on anatomical landmark-based features

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Embodiment Construction

[0026] While examples and features of the disclosed principles are described herein, modifications, adaptations and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. Furthermore, the words "having," "comprising," and "including," and other similar forms, are intended to be equivalent in meaning and are open such that one or more items following any of these words do not refer to the same or more Multiple terms are exhaustive, or means limited to one or more listed terms. And the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly dictates otherwise.

[0027]Systems and methods consistent with the present disclosure are directed to segmenting medical images using landmark feature-based learning algorithms. As used herein, "learning algorithm" refers to any algorithm that can learn a model or pattern based on existing information or knowledge. For example, the learning...

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Abstract

The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical images. Embodiments of the present disclosure may relate to a method for segmenting medical images. The method may be implemented by a processor device executing a plurality of computer executable instructions. The method may comprise receiving an image from a memory, and identifying at least one landmark point within the image. The method may further comprise selecting an image point in the image, and determining at least one feature for the image point relative to the at least one landmark point. The method may also comprise associating the image point with an anatomical structure by using a classification model based on the at least one determined feature.

Description

technical field [0001] The present disclosure relates generally to medical image segmentation. More specifically, the present disclosure relates to systems and methods for automated medical image segmentation based on learning algorithms using features extracted relative to anatomical landmarks. Background technique [0002] Image segmentation techniques are widely used to segment medical images and determine contours between anatomical structures within images. For example, in radiation therapy, automatic segmentation of organs is often performed to reduce contouring time and improve contour accuracy and consistency across hospitals. However, automated segmentation is still a very difficult task on medical images with lower image quality, such as some computed tomography (CT) or cone beam computed tomography (CBCT) images that can be used to treat cancer patients. For example, such CT or CBCT images are known to have low contrast and fine texture for most soft tissue stru...

Claims

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Application Information

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IPC IPC(8): G06T7/11
CPCG06T2207/30081G06T2207/20081G06T7/10G06T7/11G06F16/583A61B6/00G06F18/2411
Inventor 韩骁周燕
Owner ELEKTA AB
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