US 12,073,564 B2
Method for automatic segmentation of fuzzy boundary image based on active contour and deep learning
Junying Chen, Guangzhou (CN); and Haijun You, Guangzhou (CN)
Assigned to SOUTH CHINA UNIVERSITY OF TECHNOLOGY, Guangzhou (CN)
Appl. No. 17/641,445
Filed by SOUTH CHINA UNIVERSITY OF TECHNOLOGY, Guangzhou (CN)
PCT Filed Oct. 31, 2020, PCT No. PCT/CN2020/125703
§ 371(c)(1), (2) Date Mar. 9, 2022,
PCT Pub. No. WO2021/047684, PCT Pub. Date Mar. 18, 2021.
Claims priority of application No. 201910846367.8 (CN), filed on Sep. 9, 2019.
Prior Publication US 2022/0414891 A1, Dec. 29, 2022
Int. Cl. G06K 9/00 (2022.01); A61K 35/12 (2015.01); G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06T 7/149 (2017.01); G06T 7/62 (2017.01)
CPC G06T 7/149 (2017.01) [G06T 7/11 (2017.01); G06T 7/12 (2017.01); G06T 7/62 (2017.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20116 (2013.01); G06T 2207/20161 (2013.01); G06T 2207/30004 (2013.01)] 8 Claims
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1. A method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning, comprising the following steps:
S0, generate a medical image by an ultrasound system, wherein the medical image is a fuzzy boundary image;
S1, segmenting the fuzzy boundary image using a deep learning model to obtain an initialized target segmentation result; and
S2, fine-tuning the segmentation result of the model using an active contour model to obtain a more accurate normal boundary and fuzzy boundary segmentation result, the step specifically comprising:
S2.1, initializing the active contour model using a region boundary in the initialized target segmentation result obtained in S1 to construct an initial level set, wherein
the initial level set ϕ1(x, y) of the active contour model is constructed from the segmentation result of the deep learning model, and the initial level set is defined as follows:

OG Complex Work Unit Math
where R(x, y)={0,1} is the segmentation result of the deep learning model, R(x, y)=0 indicates that a point (x, y) belongs to a target region, and R(x, y)=1 indicates that the point (x, y) belongs to a non-target region; and points at a demarcation between the target region and the non-target region form a target boundary B, and D(x, y) is the shortest distance between each point (x, y) on the image and the target boundary B;
S2.2, using the level set to represent an energy function, and obtaining a partial differential equation for curve evolution through the energy function;
S2.3, performing a judgment of a region in which a contour point is located; and
S2.4, after determining a region in which each contour point is located, calculating a value of the partial differential equation and evolving a contour through iterations until a maximum number of iterations is reached or the contour changes slightly or does not change, and then completing the segmentation.