System and method and medium for segmenting medical images
A medical image and image technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problem of time-consuming image segmentation
Active Publication Date: 2020-10-02
BEIJING CURACLOUD TECH CO LTD
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Problems solved by technology
Although U-Net and its related methods provide high accuracy, when it comes to large 3D medical images such as lung CT scans, their processing of the whole image segmentation is still time-consuming
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[0021] Reference will now be made in detail to the exemplary embodiments, 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.
[0022] figure 2 The figure illustrates an exemplary image segmentation system 200 according to some embodiments of the present disclosure. According to the present disclosure, the image segmentation system 200 is configured to segment medical images acquired by the image acquisition device 205 . According to the present disclosure, image segmentation system 200 may receive medical images from image acquisition device 205 . Alternatively, the medical images may first be stored in an image database, such as medical image database 204, and image segmentation system 200 may receive the medical images from the image database. In some embodiments, the medical images may be two-dimensional (2D) or three-dimensional (3D) image...
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Embodiments of the present disclosure provide systems and methods for segmenting medical images and media. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes a memory configured to store a multi-level learning network including at least a first convolutional block and a second convolutional block. The second convolution block has at least one convolutional layer. The system also includes a processor. The processor is configured to determine a first feature mapping by applying the first convolutional block to the medical image, and to determine a second feature mapping by applying the second convolutional block to the first feature mapping. The processor is further configured todetermine a first level of feature mapping by combining the first feature mapping and the second feature mapping. The processor is further configured to obtain a first level of the segmented image based on the first level of feature mapping.
Description
[0001] Cross References to Related Applications [0002] This application is based on and claims priority to U.S. Provisional Application No. 62 / 578,907, filed October 30, 2017, which is hereby incorporated by reference in its entirety. technical field [0003] The present disclosure relates to systems and methods for medical image segmentation, and more particularly, to systems and methods for medical image segmentation using multi-level learning networks including convolutional ladders. Background technique [0004] Accurate segmentation of medical images is a critical step in the delineation of radiotherapy planning. Image segmentation is the process of dividing a digital image into parts. The goal of segmentation is to simplify the representation of an image and / or change it into something more meaningful and easier to analyze. Image segmentation is usually used to locate objects and boundaries (lines / curves, etc.) in an image. More precisely, image segmentation is th...
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IPC IPC(8): G06T7/10
CPCG06T7/10G06T2207/10081G06T2207/10088G06T2207/10116G06T2207/20081G06T2207/20084
Inventor 宋麒陈翰博孙善辉尹游兵
Owner BEIJING CURACLOUD TECH CO LTD