Method for automatic segmentation of fuzzy boundary image based on active contour and deep learning

Patent metadata

Field	Value
Publication number	US-12073564-B2
Application number	US-202017641445-A
Country	US
Kind code	B2
Filing date	Oct 31, 2020
Priority date	Sep 9, 2019
Publication date	Aug 27, 2024
Grant date	Aug 27, 2024

Abstract

The present invention discloses a method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning. In the method, firstly, a fuzzy boundary image is segmented using a deep convolutional neural network model to obtain an initial segmentation result; then, a contour of a region inside the image segmented using the deep convolutional neural network model is used as an initialized contour and a contour constraint of an active contour model; and the active contour model drives, through image characteristics of a surrounding region of each contour point, the contour to move towards a target edge to derive an accurate segmentation line between a target region and other background regions. The present invention introduces an active contour model on the basis of a deep convolutional neural network model to further refine a segmentation result of a fuzzy boundary image, which has the capability of segmenting a fuzzy boundary in the image, thus further improving the accuracy of segmentation of the fuzzy boundary image.

First claim

What is claimed is:

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 ϕ 1 (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:

ϕ
I

(

x
,
y

)

=

{

D
⁡
(

x
,
y

)

,

R
⁡
(

x
,
y

)

=
0

-

D
⁡
(

x
,
y

)

,

R
⁡
(

x
,
y

)

=
1

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.

2. The method for automatic segmentation of a fuzzy boundary image based on active contour and deep learning of claim 1 , wherein in step S2.2, a total of three parts are included in the energy function: 1) the perimeter and area of the contour; 2) a contour local region energy; and 3) a contour constraint energy; and
the whole energy function is defined as follows:

F
=

u
·
Length

⁢

(
C
)

+

v
·
Area

⁢

(

inside
⁢

(
C
)

)

+

λ
1

(

∑

p
∈

B
⁡
(
C
)

∫

ia
⁡
(
p
)

❘
"\[LeftBracketingBar]"

μ
0

(

x
,
y

)

-

c
ip

❘
"\[RightBracketingBar]"

2

⁢

Assignees

• Univ South China Tech

Inventors

• Chen Junying
• You Haijun

Classifications

• G06T2207/30004

  Biomedical image processing · CPC title

• G06T2207/20161

  Level set · CPC title

• G06T2207/20116

  Active contour; Active surface; Snakes · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title