CN112164075A - Segmentation method for maxillary sinus membrane morphological change - Google Patents

Segmentation method for maxillary sinus membrane morphological change Download PDF

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CN112164075A
CN112164075A CN202011012803.0A CN202011012803A CN112164075A CN 112164075 A CN112164075 A CN 112164075A CN 202011012803 A CN202011012803 A CN 202011012803A CN 112164075 A CN112164075 A CN 112164075A
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maxillary sinus
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CN112164075B (en
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李康荣
熊体超
凌永权
唐会
吴志杰
庞康高
李观华
楊偉勤
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Guangdong University of Technology
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Abstract

The invention provides a segmentation method aiming at the form change of a maxillary sinus membrane, which comprises the steps of segmenting and extracting a maxillary sinus air cavity model and a maxillary sinus bone cavity initial model from an obtained maxillary sinus three-dimensional sectional image, scanning the inner surface of the maxillary sinus bone cavity initial model by using a vertex screening method to obtain an inner surface vertex, screening and deleting abnormal vertices by using an average vertex distance filtering method, and constructing a vertex curved surface by using the screened vertices. Attaching the vertex curved surface to the inner surface of the maxillary sinus bone cavity initial model to generate a maxillary sinus bone cavity reconstruction model; and subtracting the maxillary sinus air cavity model from the maxillary sinus bone cavity reconstruction model to obtain the segmentation result of the cyst or the mucosa. The invention can effectively separate cyst or mucosa with the thickness less than 2 mm.

Description

Segmentation method for maxillary sinus membrane morphological change
Technical Field
The invention relates to the field of biomedical images, in particular to a segmentation method aiming at morphological changes of a maxillary sinus membrane.
Background
Recently, studies on the association of abnormal morphological changes in the morphology and location of the cyst and mucosal structures of the maxillary sinus with dental lesions using CBCT (cone beam computed tomography) images have been receiving attention. The maxillary sinuses are a pair of sinuses of the maxilla located below the eyes, which are surrounded by four adjacent bones: alveolar process of maxilla, zygomatic bone, infraorbital wall and lateral wall of nasal cavity. Geometrically, the maxillary sinus is a pyramidal structure with its apex at the lateral boundary of the zygomatic bone and base at the intranasal boundary, and its sagittal slice shows how its cross section changes from triangular to quadrangular. The maxillary sinus cavity has a large number of openings and incomplete thin bone boundaries, which bring great difficulty to the cyst segmentation in the sinus cavity. Thus, accurate cutting and quantification of mucosal thickening provides a basis for exploring pathological findings in greater detail in the future. The most common morphological changes of the maxillary sinus are cysts and thickened mucosa, and in order to quantify the thickening of the mucosa and the thickness of the cysts, a mucosal segmentation of the sinus cavity of the maxillary sinus should be obtained first. For the segmentation of cysts (more than 5mm), the region growing method can be suitable for most cyst segmentation and can be accurately cut by using a segmentation tool; however, for the mucosa hyperplasia of the maxillary sinus with the mucosa thickness less than 2mm, the general algorithm for region growing cannot accurately segment, and the maxillary sinus mucosa hyperplasia usually grows insufficiently or overgrows.
Another approach is to use an active contour multi-object segmentation tool, such as the robust statistical segmenter in 3d selier, whose iterative evolution is driven by local robust statistical data, updating the median and quartile spacing in each iteration. It is more resistant to noise and intensity non-uniformities, and is an effective interactive medical image segmentation method using a fast growth, where the user manually draws labels on the target segmented region of the slice image and then grows automatically up to the cyst boundary. However, the abnormal structure of the maxillary sinus may cause the loss of the bone boundary of the bone cavity of the maxillary sinus, the maxillary sinus with cyst or mucosa thickening symptoms may have structural deformation of various degrees, and meanwhile, the thin bone of the wall of the posterior sinus cannot be extracted by the thresholding segmentation, which may cause a large hole and a wide mouth and a large number of openings existing at one side of the maxillary sinus close to the nasal sinus, which may generally cause growth leakage at the fracture boundary of the opening and the thin bone. In addition, for cysts with small thickness (< 3mm), the difficulty of growth and segmentation is further complicated, the shape of the mucosa is more irregular, the thickness is smaller, and the segmentation difficulty is very large, which brings great difficulty to the mucosa or cyst segmentation algorithm.
Chinese patent CN109886969A published in 2019, 6, month and 14 provides a three-dimensional automatic segmentation method of an endoscopic optical coherence tomography image of a respiratory tract, which comprises two steps of image preprocessing and respiratory tract tissue segmentation; the method comprises the following steps: (1) image preprocessing, specifically: (1.1) denoising; (1.2) removing the plastic protective sleeve; (1.3) carrying out binarization and region filtering; (1.4) detecting an upper boundary; (2) the respiratory tract tissue segmentation method comprises the following specific steps: (2.1) detecting the boundary of the respiratory tract lumen surface in the image by using a three-dimensional map search algorithm; (2.2) using a three-dimensional map search method to divide mucosa and submucosal tissues in different search regions, respectively. The method can rapidly and smoothly obtain mucosa segmentation result, but the thickness of the segmented mucosa is not thin enough.
Disclosure of Invention
The invention provides a segmentation method aiming at the morphological change of a maxillary sinus membrane, aiming at overcoming the defect that the thickness of mucosa or cyst of the maxillary sinus segmented by the prior art is not thin enough.
The technical scheme of the invention is as follows:
the invention provides a segmentation method aiming at the morphological change of a maxillary sinus membrane, which comprises the following steps:
s1: obtaining a three-dimensional tomography image of the maxillary sinus by using a CT scanning technology, and recording the three-dimensional tomography image as R;
s2: extracting a maxillary sinus air cavity model marked as A by segmentation from the maxillary sinus three-dimensional sectional image R;
s3: preliminarily segmenting and extracting a maxillary sinus bone cavity initial model from the maxillary sinus three-dimensional sectional image R, and marking as B;
s4: taking a point protruding from the inner surface of the maxillary sinus bone cavity initial model B as a vertex, and constructing a vertex curved surface by using the vertex;
s5: and converting the vertex curved surface into a label voxel and fitting the label voxel onto the inner surface of the initial model B of the maxillary sinus bone cavity to form a maxillary sinus bone cavity reconstruction model, recording the model as C, subtracting the maxillary sinus air cavity model A from the maxillary sinus bone cavity reconstruction model C, and obtaining a division result of mucosa or cyst.
Preferably, the specific operation method for extracting and segmenting the maxillary sinus air cavity model a in S2 is as follows: segmentation was performed using a Robust statistical Segmenter (Robust Statistics Segmenter) in the multi-object segmentation tool (3D Slicer).
Preferably, the specific steps of S3 are:
s3.1: scanning all voxels in the three-dimensional tomography image R of the maxillary sinus, dividing the voxels into three fields according to the intensity values of the voxels; setting intensity value thresholds p and q, wherein voxels are divided into the air field when the voxel intensity value is less than p, voxels are divided into the soft tissue field when the voxel intensity value is between p and q, and voxels are divided into the bone field when the voxel intensity value is greater than q;
s3.2: scanning the voxels which are divided into the soft tissue field again to ensure that the voxels of which the voxel intensity values are close to the intensity value threshold q are accurately divided;
s3.3: voxels belonging to the bone domain are labeled and an initial model B of the maxillary sinus bone cavity is generated.
Preferably, the specific method for dividing the voxels in S3.1 is as follows: the division is carried out by a fuzzy C-mean method.
Preferably, the specific steps of S4 are:
s4.1: scanning the inner surface of the initial maxillary sinus bone cavity model B;
s4.2: defining the vertex set of the initial model B of the bone cavity of the maxillary sinus as follows:
Figure BDA0002698012430000031
Figure BDA0002698012430000032
all the vertexes of the inner surface of the B,
Figure BDA0002698012430000033
respectively the coordinates and normal vectors of the vertex in the 3D Cartesian space;
s4.3: scanning and selecting a vertex;
for each slice along the path, representing the slice center O as the origin, with coordinates (0, 0, 0); equally dividing the slice into M sectors, wherein the angle of each sector is thetathAnd defining a direction vector for each sector as
Figure BDA0002698012430000034
At each angle thetathIn the sector part, calculating the distance from each vertex to the circle center O, and selecting the vertex with the minimum distance; let the normal vector of the scanned vertex be
Figure BDA0002698012430000035
When in use
Figure BDA0002698012430000036
And the fan-shaped direction vector
Figure BDA0002698012430000037
Angle between theta < thetathThen, the vertex is selected preliminarily; if the vertex is finally selected, the following two conditions are required:
the vertex faces the origin O, i.e.:
Figure BDA0002698012430000038
normal vector of vertex
Figure BDA0002698012430000039
Normal vector at scanned vertex
Figure BDA00026980124300000310
The upper projection angle phi is smaller than the threshold value phith，
Figure BDA00026980124300000311
S4.4: screening the vertexes obtained in the S4.3 by using an average vertex distance filtering method and deleting abnormal vertexes;
s4.5: and after the vertex screening is finished, constructing a vertex curved surface by using the vertex.
Preferably, the specific steps of S4.4 are as follows:
the distance from a vertex to the center O in a sector of a slice is d (i), and the average of the distances from all vertices to the center O in a sector is dsect-mean(k) Then, then
Figure BDA00026980124300000312
Wherein, i ═ 1.., Nk，NkIs the number of vertices in the sector;
let d be the sector of the slicesect-mean(k) Has an average value of dslice-meanThen, then
Figure BDA00026980124300000313
Wherein k is 1.., M is the number of the sector parts; if | dsect-mean(k)-dslice-mean|＜daThe vertices in the corresponding sector will be preserved, daIs a global threshold;
d of each sectorsect-mean(k) D of the preceding three sectorssect-mean(k) For further comparison, if the difference is denoted as z (m), then z (m) ═ dsect-mean(k)-dsect-mean(k + m) |, wherein m ═ 1, 2, 3; if any one of Z (m) > dlThe vertices in the corresponding sector are considered as outlier fixed points and are deleted, dlIs a local threshold.
Preferably, before S4.1 is implemented, any 4 misaligned points need to be marked on the nasal wall surface in the initial maxillary sinus cavity model B: m1, M2, M3 and M4, and marking any point Apex outside the nasal wall surface in the initial model B of the maxillary sinus bone cavity, calculating the geometric constraint relation of the initial model B of the maxillary sinus bone cavity by using the 5 marked points, and generating a slice path.
Preferably, in S4.1, the scanning operation is implemented using a vertex screening algorithm.
Preferably, the vertex surfaces in S4.5 are constructed by poisson reconstruction (poissons reconstruction).
Preferably, the specific operation method for converting the vertex curved surface into the label voxel in S5 is as follows: the translation is implemented using Model to label mapping (Model to label map) in the multi-object segmentation tool (3D Slicer).
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the method screens peaks which meet the requirements of the inner surface of the initial model of the maxillary sinus bone cavity, constructs a peak curved surface through the peaks, further generates a maxillary sinus bone cavity reconstruction model, and subtracts the maxillary sinus air cavity model from the maxillary sinus bone cavity reconstruction model to obtain the segmentation result of cyst or mucosa; the thickness of the cyst or the mucosa divided by the invention is far smaller than that of the cyst or the mucosa divided by the prior art.
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Fig. 1 is a flowchart of a segmentation method for morphological changes of maxillary sinus membrane according to embodiment 1.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The present embodiment provides a segmentation method for morphological changes of maxillary sinus membrane, as shown in fig. 1, the method comprises the following steps:
s1: obtaining a three-dimensional tomography image of the maxillary sinus by using a CT scanning technology, and recording the three-dimensional tomography image as R;
s2: extracting a maxillary sinus air cavity model marked as A by segmentation from the maxillary sinus three-dimensional sectional image R;
s3: preliminarily segmenting and extracting a maxillary sinus bone cavity initial model from the maxillary sinus three-dimensional sectional image R, and marking as B;
s4: taking a point protruding from the inner surface of the maxillary sinus bone cavity initial model B as a vertex, and constructing a vertex curved surface by using the vertex;
s5: and converting the vertex curved surface into a label voxel and fitting the label voxel onto the inner surface of the initial model B of the maxillary sinus bone cavity to form a maxillary sinus bone cavity reconstruction model, recording the model as C, subtracting the maxillary sinus air cavity model A from the maxillary sinus bone cavity reconstruction model C, and obtaining a division result of mucosa or cyst.
The specific operation method for extracting and segmenting the maxillary sinus air cavity model A in the S2 comprises the following steps: segmentation is performed using a Robust statistical Segmenter (Robust Statistics Segmenter) in a multi-object segmentation tool.
The specific steps of S3 are as follows:
s3.1: : scanning all voxels in the three-dimensional tomography image R of the maxillary sinus, dividing the voxels into three fields according to the intensity values of the voxels; setting intensity value thresholds p and q, wherein voxels are divided into the air field when the voxel intensity value is less than p, voxels are divided into the soft tissue field when the voxel intensity value is between p and q, and voxels are divided into the bone field when the voxel intensity value is greater than q;
s3.2: scanning the voxels which are divided into the soft tissue field again to ensure that the voxels of which the voxel intensity values are close to the intensity value threshold q are accurately divided;
s3.3: voxels belonging to the bone domain are labeled and an initial model B of the maxillary sinus bone cavity is generated.
The specific method for dividing the voxels in the S3.1 comprises the following steps: and adopting a fuzzy C-mean algorithm for division.
The specific steps of S4 are as follows:
s4.1: scanning the inner surface of the initial maxillary sinus bone cavity model B;
s4.2: defining the vertex set of the initial model B of the bone cavity of the maxillary sinus as follows:
Figure BDA0002698012430000051
Figure BDA0002698012430000052
all the vertexes of the inner surface of the B,
Figure BDA0002698012430000053
respectively the coordinates and normal vectors of the vertex in the 3D Cartesian space;
s4.3: scanning and selecting a vertex;
for each slice along the path, representing the slice center O as the origin, with coordinates (0, 0, 0); equally dividing the slice into M sectors, wherein the angle of each sector is thetathAnd defining a direction vector for each sector as
Figure BDA0002698012430000054
In the present embodiment, the slice is equally divided into 90 slices, and each sector has an angle of 4 °, i.e., M is 90, θth＝4°；
At each angle thetathIn the sector part, calculating the distance from each vertex to the circle center O, and selecting the vertex with the minimum distance; let the normal vector of the scanned vertex be
Figure BDA0002698012430000061
When in use
Figure BDA0002698012430000062
And the fan-shaped direction vector
Figure BDA0002698012430000063
Angle between theta < thetathThen, the vertex is selected preliminarily; if the vertex is finally selected, the following two conditions are required:
the vertex faces the origin O, i.e.:
Figure BDA0002698012430000064
normal vector of vertex
Figure BDA0002698012430000065
Normal vector at scanned vertex
Figure BDA0002698012430000066
The upper projection angle phi is smaller than the threshold value phith，
Figure BDA0002698012430000067
S4.4: screening the vertexes obtained in the S4.3 by using an average vertex distance filtering method and deleting abnormal vertexes;
s4.5: and after the vertex screening is finished, reconstructing the curved surface by using the vertex.
The S.4.4 comprises the following specific steps:
the distance from a vertex to the center O in a sector of a slice is d (i), and the average of the distances from all vertices to the center O in a sector is dsect-mean(k) Then, then
Figure BDA0002698012430000068
Wherein, i ═ 1.., Nk，NkIs the number of vertices in the sector;
let d be the sector of the slicesect-mean(k) Has an average value of dslice-meanThen, then
Figure BDA0002698012430000069
Wherein k is 1.., M is the number of the sector parts; in the embodiment, the number of the fan-shaped parts is 90, namely M is 90; if | dsect-mean(k)-dslice-mean|＜daThe vertices in the corresponding sector will be preserved, daIs a global threshold; the global threshold in this embodiment is 5, da＝5；
D of each sectorsect-mean(k) D of the preceding three sectorssect-mean(k) For further comparison, if the difference is denoted as z (m), then z (m) ═ dsect-mean(k)-dsect-mean(k + m) |, wherein m ═ 1, 2, 3; if Z (m) > dlThe vertices in the corresponding sector are considered as outlier fixed points and are deleted, dlIs a local threshold; in this embodiment the local threshold is 3, dl＝3；
Before the implementation of the S4.1, any 4 misaligned points need to be marked on the nasal wall surface in the maxillary sinus bone cavity initial model B: m1, M2, M3 and M4, and marking any point Apex outside the nasal wall surface in the initial model B of the maxillary sinus bone cavity, calculating the geometric constraint relation of the initial model B of the maxillary sinus bone cavity by using the 5 marked points, and generating a slice path.
In S4.1, a vertex screening algorithm is used to implement the scanning operation.
And adopting Poisson reconstruction (PoissonsourceReconstruction) when the vertex curved surface in the S4.5 is constructed.
The vertices selected in S4.5 were mirrored on the mirror plane of the nasal wall constructed from M1-M4 when reconstructed.
The specific operation method of converting the reconstructed surface into the label voxel in S5 is to use a Model to label map (Model to label map) in a multi-object segmentation tool (3D Slicer) to implement the conversion.
The technical scheme of the embodiment has the beneficial effects that: in the embodiment, the vertexes of the internal surface of the initial model of the maxillary sinus bone cavity, which meet the requirements, are screened out, the vertex curved surface is constructed through the vertexes, the maxillary sinus bone cavity reconstruction model is further generated, and the maxillary sinus bone cavity reconstruction model is used for subtracting the maxillary sinus air cavity model to obtain the segmentation result of the cyst or mucosa; the cyst or mucosa with the thickness less than 2mm can be effectively separated.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A segmentation method aiming at the morphological change of a maxillary sinus membrane is characterized by comprising the following steps:
s1: obtaining a three-dimensional tomography image of the maxillary sinus by using a CT scanning technology, and recording the three-dimensional tomography image as R;
s2: extracting a maxillary sinus air cavity model marked as A by segmentation from the maxillary sinus three-dimensional sectional image R;
s3: preliminarily segmenting and extracting a maxillary sinus bone cavity initial model from the maxillary sinus three-dimensional sectional image R, and marking as B;
s4: taking a point protruding from the inner surface of the maxillary sinus bone cavity initial model B as a vertex, and constructing a vertex curved surface by using the vertex;
s5: and converting the vertex curved surface into a label voxel and fitting the label voxel onto the inner surface of the initial model B of the maxillary sinus bone cavity to form a maxillary sinus bone cavity reconstruction model, recording the model as C, subtracting the maxillary sinus air cavity model A from the maxillary sinus bone cavity reconstruction model C, and obtaining a division result of mucosa or cyst.
2. The method for segmenting maxillary sinus membrane morphological change according to claim 1, wherein the S2 is implemented by extracting a maxillary sinus air cavity model a by: segmentation is performed using a robust statistical segmenter in a multi-object segmentation tool.
3. The method for segmenting the morphological change of the maxillary sinus membrane according to the claim 2, wherein the specific steps of the step S3 are as follows:
s3.1: scanning all voxels in the three-dimensional tomography image R of the maxillary sinus, dividing the voxels into three fields according to the intensity values of the voxels; setting intensity value thresholds p and q, wherein voxels are divided into the air field when the voxel intensity value is less than p, voxels are divided into the soft tissue field when the voxel intensity value is between p and q, and voxels are divided into the bone field when the voxel intensity value is greater than q;
s3.2: scanning the voxels which are divided into the soft tissue field again to ensure that the voxels of which the voxel intensity values are close to the intensity value threshold q are accurately divided;
s3.3: voxels belonging to the bone domain are labeled and an initial model B of the maxillary sinus bone cavity is generated.
4. A segmentation method aiming at the morphological change of the maxillary sinus membrane according to claim 3, wherein the specific method for dividing the voxels in S3.1 is as follows: and adopting a fuzzy C-mean algorithm for division.
5. The method for segmenting maxillary sinus membrane morphological change according to claim 4, wherein the specific steps of S4 are as follows:
s4.1: scanning the inner surface of the initial maxillary sinus bone cavity model B;
s4.2: defining the vertex set of the initial model B of the bone cavity of the maxillary sinus as follows:
Figure FDA0002698012420000021
Figure FDA0002698012420000022
all the vertexes of the inner surface of the B,
Figure FDA0002698012420000023
respectively the coordinates and normal vectors of the vertex in the 3D Cartesian space;
s4.3: scanning and selecting a vertex;
for each slice along the path, representing the slice center O as the origin, with coordinates (0, 0, 0); equally dividing the slice into M sectors, wherein the angle of each sector is thetathAnd defining a direction vector for each sector as
Figure FDA0002698012420000024
At each angle thetathIn the sector part, calculating the distance from each vertex to the circle center O, and selecting the vertex with the minimum distance; let the normal vector of the scanned vertex be
Figure FDA0002698012420000025
When in use
Figure FDA0002698012420000026
And the fan-shaped direction vector
Figure FDA0002698012420000027
Angle between theta < thetathThen, the vertex is selected preliminarily; if the vertex is finally selected, the following two conditions are required:
the vertex faces the origin O, i.e.:
Figure FDA0002698012420000028
normal vector of vertex
Figure FDA0002698012420000029
Normal vector at scanned vertex
Figure FDA00026980124200000210
The upper projection angle phi is smaller than the threshold value phith，
Figure FDA00026980124200000211
S4.4: screening the vertexes obtained in the S4.3 by using an average vertex distance filtering method and deleting abnormal vertexes;
s4.5: and after the vertex screening is finished, constructing a vertex curved surface by using the vertex.
6. The method for segmenting the morphological change of the maxillary sinus membrane according to the claim 6, wherein the S4.4 comprises the following specific steps:
the distance from a vertex to the center O in a sector of a slice is d (i), and the average of the distances from all vertices to the center O in a sector is dsect-mean(k) Then, then
Figure FDA00026980124200000212
Wherein, i ═ 1.., Nk，NkIs the number of vertices in the sector;
let d be the sector of the slicesect-mean(k) Has an average value of dslice-meanThen, then
Figure FDA00026980124200000213
Wherein k is 1.., M is the number of the sector parts; if | dsect-mean(k)-dslice-mean|＜daThe vertices in the corresponding sector will be preserved, daIs a global threshold;
d of each sectorsect-mean(k) D of the preceding three sectorssect-mean(k) For further comparison, if the difference is denoted as z (m), then z (m) ═ dsect-mean(k)-dsect-mean(k + m) |, wherein m ═ 1, 2, 3; if any one of Z (m) > dlThe vertices in the corresponding sector are considered as outlier fixed points and are deleted, dlIs a local threshold.
7. The method for segmenting maxillary sinus membrane morphological change according to claim 6, wherein before S4.1 is implemented, any 4 misaligned points are marked on the nasal wall surface in the maxillary sinus bone cavity initial model B: m1, M2, M3 and M4, and marking any point Apex outside the nasal wall surface in the initial model B of the maxillary sinus bone cavity, calculating the geometric constraint relation of the initial model B of the maxillary sinus bone cavity by using the 5 marked points, and generating a slice path.
8. The method for segmenting the morphological change of the maxillary sinus membrane according to the claim 7, wherein the scanning operation is implemented by using a vertex screening algorithm in the S4.1.
9. The method for segmenting the morphological change of the maxillary sinus membrane according to the claim 8, wherein the vertex curved surface in the S4.5 is constructed by Poisson reconstruction.
10. A segmentation method for morphological changes of maxillary sinus membrane according to claim 9, wherein the specific operation method of converting vertex curved surface into label voxel in S5 is as follows: the transformation is achieved using model-to-label mapping in a multi-object segmentation tool.
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