US 12,602,788 B2 · Method and system for fully automatically segmenting cerebral cortex surface based on graph network

Abstract

A method and a system for fully automatically segmenting a cerebral cortex surface based on a graph network, the method including: registering a cerebral magnetic resonance image to a standard template space; performing cerebral cortex surface reconstruction on the registered cerebral magnetic resonance image based on a deep neural network; calculating adjacency matrices among the vertexes of the reconstructed cerebral cortex; acquiring corresponding distinguishing features of each grid vertex of the cerebral cortex surface as corresponding feature vectors of the point so as to obtain a cerebral cortex surface segmentation result; and mapping the cerebral cortex surface segmentation result back to the original coordinate space from the standard template space. Graph structure modeling is performed on the reconstructed cerebral cortex surface, and the global topological structure features are learned based on the graph network, thus realizing the accurate segmentation of the cerebral cortex surface.

A method for fully automatically segmenting a cerebral cortex surface based on a graph network, comprising:

registering a cerebral magnetic resonance image to a standard template space;
performing cerebral cortex surface reconstruction on the registered cerebral magnetic resonance image based on a deep neural network, and acquiring triangular patch grid representations of the cerebral cortex surface;
performing a 4× down-sampling on cerebral cortex surface grid data;
calculating adjacency matrices in a corresponding convolution process of the graph network;
calculating feature description vectors of vertexes of the cerebral cortex surface, and generating corresponding feature matrices of all vertexes of the cerebral cortex surface;
inputting the adjacency matrices and the feature matrices to a trained graph network model to obtain a cerebral cortex surface segmentation result;
and
performing post-processing and space mapping on the segmentation result to complete the segmentation of the cerebral cortex surface, wherein
the calculating adjacency matrices in a corresponding convolution process of the graph network comprises:
calculating a Euclidean distance and a geodesic distance among the cerebral cortex vertexes according to a connection relationship among the cerebral cortex surface grid vertexes after down-sampling and corresponding Euclidean space coordinates of the vertexes, and acquiring corresponding adjacency matrices of three convolution layers in the graph network according to a weight calculation formula and by virtue of three Gaussian kernels initialized by pre-training parameters, and
specifically, the calculating 3 adjacency matrices among the cerebral cortex vertexes for the corresponding convolution process of the graph network according to the connection relationship among the vertexes and the geodesic distance comprises:
calculating a Euclidean distance d and a geodesic distance ρ among the cerebral cortex vertexes according to the connection relationship among the down-sampled cerebral cortex surface grid vertexes and corresponding Euclidean space coordinates of the vertexes;
acquiring the corresponding adjacency matrices Ā1, Ā2, Ā3 of the three convolution layers in the graph network according to a weight calculation formula and by virtue of the three Gaussian kernels initialized by pre-training parameters;
and giving coordinates of two vertexes, shown as:
vi=(xi, yi, zi) and vj=(xj, yj, zj), the calculation formula of the Euclidean distance di,j being:
d
i
,
j
=

v
i
-
v
j

2
2
=
(
x
i
-
x
j
)
2
+
(
y
i
-
y
j
)
2
+
(
z
i
-
z
j
)
2
2
,
wherein the geodesic distance ρ is calculated by using a classical Dijkstra algorithm;
a relationship vector between any two vertexes vi and vj is ui,j=(di,j,ρi,j)T, i≠j;
and the calculation of adjacency weight coefficients of two non-overlapped vertexes is generated by inputting the relationship vectors into the Gaussian kernel functions, and the specific calculation formula is as follows:
w
i
,
j
=
exp
⁡
(
-
1
2
⁢
(
u
-
u
_
i
,
j
)
T
⁢
(
σ
¯
d
2
σ
¯
ρ
2
)
-
1
⁢
(
u
-
u
_
i
,
j
)
)
and i≠j, an adjacency coefficient between any two vertexes is shown as:
a
i
,
j
=
a
j
,
i
=
{
w
i
,
j
i
≠
j
1
i
=
j
;
the adjacency matrix Ã can be shown as:
A
_
=
(
1
a
1
,
2
…
a
1
,
N
a
2
,
1
1
…
a
2
,
N
⋮
⋮
⋱
⋮
a
N
,
1
a
N
,
2
…
1
)
;
and the cerebral cortex surface formed by points and edges is shown as G=(V, E), V representing a set of all the vertexes, and E representing a set corresponding to all the edges;
(xi, yi, zi) represents position coordinates of the edge v;
in a three-dimensional template space;
and the obtained adjacency matrices are square matrices having a dimension of N*N, N representing the number of the vertexes of the cortex surface.

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Method and system for fully automatically segmenting cerebral cortex surface based on graph network

Life of this patent

Method and system for fully automatically segmenting cerebral cortex surface based on graph network

Life of this patent

Abstract

Claims8