Cell image segmentation method based on graph path search and deep learning

A path search and deep learning technology, applied in the fields of biomedicine and computer image processing, which can solve the problems of large task volume, unfavorable research, and unsatisfactory cell image segmentation effect.

Active Publication Date: 2019-11-15
TSINGHUA UNIV
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Problems solved by technology

Since the training cell images often require manual annotation by doctors or medical students, and the workload is large, when the number of cells contained in the cell image is large, it is very difficult to use the instance segmentation network method to segment
In addition, the information of the cell i

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  • Cell image segmentation method based on graph path search and deep learning
  • Cell image segmentation method based on graph path search and deep learning
  • Cell image segmentation method based on graph path search and deep learning

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[0028] In order to further illustrate the technical means and effects of the present invention for solving technical problems, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the provided accompanying drawings are schematic and mutually exclusive They are not drawn to scale or scale, and therefore the drawings and specific examples are not intended to limit the scope of protection claimed by the invention.

[0029] Such as figure 1 The flow of an optional embodiment of the cell image segmentation method based on graph path search and deep learning includes the following steps:

[0030] Image preprocessing S10, calculate and generate a distance map (Distance Map) based on the cell mask image marked by others according to the cell image, the calculation rule is: the pixel value of the pixel belonging to the cell is from the pixel to the nearest non-cell The Manhattan ...

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Abstract

The invention belongs to the technical field of biomedicine and computer image processing, and discloses a cell image segmentation method based on graph path search and deep learning, and the method comprises the following steps: employing a trained U-net prediction model, and carrying out the following steps: in a prediction stage, inputting a to-be-segmented cell image into the trained U-net prediction model, and predicting a distance graph of a to-be-segmented cell; labeling a cell center, and taking the pixel point with the maximum local pixel value as the cell center; searching paths, searching a plurality of paths of two adjacent cell centers, and extracting pixel values of path points; carrying out judging, comparing the pixel value of each path point on the search path with the pixel value of the cell center to judge whether the two cell centers belong to different cells or not, if not, carrying out path search between the other two adjacent cell centers, and if so, carrying out segmentation processing, and repeatedly searching until all the search is finished. According to the invention, the adherent cells in the cell image can be well distinguished and segmented.

Description

technical field [0001] The invention belongs to the technical fields of biomedicine and computer image processing, and in particular relates to a cell image segmentation method based on graph path search and deep learning. Background technique [0002] The automatic segmentation of cell images is of great significance in medical image analysis. Correctly segmenting pathological images of cells can help doctors or researchers identify each cell and study its phenotypic characteristics such as size, color, and shape. Finding out the relationship with characteristics such as genes and diseases will help researchers measure the response of cells to chemicals or in certain biological processes, thereby promoting drug development and shortening the time to market for new drugs. [0003] In recent years, with the continuous in-depth research on deep learning network, its application in cell image segmentation is increasing. There are two main types of deep learning methods for cel...

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Application Information

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IPC IPC(8): G06T7/11G06T7/136G06T7/66
CPCG06T2207/10024G06T2207/10056G06T2207/20081G06T2207/20084G06T7/11G06T7/136G06T7/66
Inventor 江瑞池宇杰
Owner TSINGHUA UNIV
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