KR20150020830A - Quantification method of vessel - Google Patents

Abstract

The present disclosure relates to a method for quantifying a blood vessel which includes the steps of: extracting the blood vessel with a 3D set of voxels based on a medical image of an organ; finding the voxels of the blood vessel included in an interest region of the organ; and quantifying the length information of the blood vessel including a diameter by using the found voxels.

Description

QUANTIFICATION METHOD OF VESSEL [0002]

Disclosure relates generally to a method for quantifying blood vessels, and more particularly, to a method for quantifying the distribution and size of blood vessels in organs by quantifying the diameter of blood vessels.

Herein, the background art relating to the present disclosure is provided, and these are not necessarily meant to be known arts.

The condition of the blood vessels can be used as an index for diseases of the organ. For example, the status of the pulmonary vasculature has emerged as a significant index for various pulmonary diseases such as pulmonary hypertension, interstitial lung disease and COPD (MG Linguraru et al., "Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary hypertension ", Medical Physics , vol. 37 (4), pp. 1522-1532, 2010).

Analysis of the pulmonary vascular status, especially the distribution and size of small pulmonary vessels, is one of the significant indexes for the pulmonary circulation status and is essential for the analysis of various pulmonary diseases.

Recent studies show that there is a correlation between the measurable morphological characteristics of pulmonary vessels, such as the diameter and area percentage of pulmonary vessels evaluated from CT images, and various clinical parameters.

Several studies have suggested that endothelial dysfunction, allegedly associated with emphysema, is closely related to vascular alteration (Santos et al., "Enhanced expression of vascular endothelial growth factor in pulmonary arteries of smokers and patients with moderate chronic obstructive pulmonary disease ", American Journal of Respiratory and Critical Care Medicine , vol. 167, pp. 1250-1256, 2003).

From a technical point of view, using the vascular contrast enhanced images has been attempted to measure the morphological characteristics for the large blood vessels (Barrier et al., "Today 's and tomorrow's imaging and circulating biomarkers for pulmonary arterial hypertension", Cellular and Molecular Life Sciences , vol. 69, pp. 2805-2831, 2012).

However, little attempt has been made to evaluate the morphological characteristics of small vessels.

On the other hand, after performing simple thresholding on 2D sectional CT images to quantify small pulmonary blood vessels, circular areas with areas smaller than 5 mm ² were selected as blood vessels, and correlation between the area of small pulmonary vessels and the pulmonary function test (PFT) (Matsuoka et al., "Quantitative computed tomographic measurement of a cross-sectional area of a small pulmonary vessel in nonsmokers without airflow limitation", Japanese Journal of Radiology , vol. 29, pp. 251-255, 2011).

However, although Matsuoka et al. Showed a strong clinical association between blood vessel distribution and PFT, it did not distinguish between pulmonary and pulmonary veins, and because of the use of 2D slice images, orthogonal to the axis of blood vessels, It is difficult to accurately measure the diameter in the direction and it is difficult to say that the result is obtained by accurate quantification of the entire lung, which is three-dimensional.

The development of medical imaging techniques, especially X-ray CT images, makes it possible to observe small structures under the millimeter in vivo. There has been rapid progress in spatial resolution as well as temporal resolution. However, it is difficult to quantify small vessels based on three-dimensional CT images through automated algorithms because of the morphologically complex structure of the vessels, such as dense distribution, close-in crossing cases, and other adjacent vessels in parallel. In particular, there has been no successful attempt to separate and quantify small vessels into arteries and veins based on three-dimensional CT images through automated algorithms.

Therefore, a method of quantifying the length information of the blood vessel including the diameter of the whole organ is required based on the three-dimensional state of the arterial and vein by the automated algorithm through the state of the blood vessel of the organ.

This will be described later in the Specification for Implementation of the Invention.

SUMMARY OF THE INVENTION Herein, a general summary of the present disclosure is provided, which should not be construed as limiting the scope of the present disclosure. of its features).

According to one aspect of the present disclosure, there is provided a method comprising: extracting a blood vessel as a three-dimensional collection of voxels based on a medical image of an organ; A step of finding voxels of a blood vessel included in a region of interest of a long term; And quantifying the length information of the blood vessel including the diameter using the found voxels.

This will be described later in the Specification for Implementation of the Invention.

BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a diagram generally illustrating an example of a method for quantifying blood vessels according to the present disclosure,
FIG. 2 is a view for explaining an example of a total process for generating a divided pulmonary vein tree,
3 is a view for explaining an example of a divided pulmonary vein tree,
4 is a view for explaining an example of a result of an algorithm for obtaining a skeleton of a divided pulmonary vein tree and extracting a node,
5 is a view for explaining an example of a method of quantifying a blood vessel using a node according to an embodiment,
6 is a view for explaining an example of a method of quantifying a blood vessel using an offset surface according to another embodiment,
7 is a view for explaining an example of a method of generating a distance field for forming an offset surface,
8 is a view for explaining an example of a process of extracting an intersection point between an inner surface and a pulmonary blood vessel,
9 is a view for explaining an example of a process of extracting a mono-oriented region for dividing a lung by a 2D schematic diagram,
10 is a view for explaining an example of root region extraction applied to volumetric CT images,
11 is a view for explaining an example of surfels calculation,
12 is a view for explaining an example of surfels calculated by 3D rendering,
13 is a diagram illustrating the accuracy of radial evaluation for virtual vascular phantom models,
Figure 14 is box plots showing the number of vessels, mean diameter, and area ratio occupied by the vessels by gradual peeling and tree branching levels methods.

The present disclosure will now be described in detail with reference to the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a view for explaining an example of a method for quantifying a blood vessel according to the present disclosure. FIG.

The method of quantifying blood vessels provides a method of observing clinically significant regions (regions of interest) in organs and quantifying length information including the diameters of blood vessels.

In the method of quantifying a blood vessel, blood vessels are extracted as a three-dimensional set of voxels on the basis of a medical image of an organ (S10). Vessels of the blood vessels contained in the region of interest of the organ are found (S40, S60, S80, S50, S70, S90). The length information of the blood vessel including the diameter is quantified using the found voxels (S100).

After the blood vessel is extracted, a process of dividing the blood vessel into a vein and an artery may be performed (S20).

The length information of the blood vessel including the diameter can be visualized in the medical image (S110).

For example, in the process of quantifying the length information of a blood vessel, the radius, or diameter, of the blood vessel is calculated using the found voxels. The area ratio of the blood vessel to the region of interest is calculated using the calculated radius of the blood vessel.

In the process of finding the voxels of a blood vessel, the level of the blood vessel can be determined according to the shape of the blood vessel or the position in the organ. Voxel voxels are found at the level selected as the region of interest.

For example, to find voxels of a blood vessel, a skeleton of a blood vessel is generated (S40). A node where the blood vessel branches off is extracted using the skeleton of the blood vessel (S60). Among the extracted nodes, voxels within a certain range spatially in the node selected as the ROI are searched (S80).

As another example, an offset surface is created (S50, S70), which is defined as a set of voxels spaced inwardly from the outer surface of the organ in order to locate the voxels of the blood vessel. Voxels corresponding to the intersection of the extracted blood vessel and the offset surface are found (S90).

The method for quantifying blood vessels according to the present disclosure can be applied to organs or organs such as lung, heart, kidney, liver, brain, and the like.

In this example, the method of quantifying pulmonary blood vessels will be mainly described.

For example, pulmonary blood vessels are extracted from volumetric chest CT images as a collection of voxels, and an initial pulmonary vascular tree is created by construction energy minimization. The mediastinal region is then cleaved from the initial pulmonary vascular tree and the branches of the initial pulmonary vascular tree are automatically segmented into sub-trees. The branches are then extended from the branches of the early pulmonary vascular tree to the truncated root regions to recombine the subtrees. Thereafter, based on the recombined initial tree, the pulmonary artery is classified into a pulmonary artery and a pulmonary vein to generate a classified pulmonary vein tree (S20).

The creation of a segmented pulmonary vein tree provides visualization of the pulmonary artery and the pulmonary vein, and then provides a basis 30 for quantifying pulmonary blood vessel length information.

The length of the pulmonary vein, for example, the small pulmonary artery and the diameter of the small pulmonary vein, is calculated from the voxels of the divided pulmonary vascular tree corresponding to the area of interest of the lung. Using the diameter, the area of the small pulmonary artery and the small pulmonary vein occupied by the surface of the lung in the region of interest of the lung, etc., can be calculated using the application.

In this example, the level of pulmonary blood vessels is determined to extract pulmonary blood vessels located in the region of interest, and the length information is evaluated for pulmonary blood vessels of the same or similar level.

FIG. 1 shows two examples of the process of determining the level of pulmonary blood vessels.

In the process of determining the level of pulmonary blood vessels according to one embodiment, the skeleton of the separated pulmonary vein tree is calculated (S40), and a branching node extraction of the pulmonary vein branches is extracted from the outer end of the lung (S60), the same similar level of pulmonary blood vessels can be collected (S80).

In the process of determining the level of pulmonary blood vessels according to another embodiment, the lungs are gradually peeled from the surface boundary of the lungs to form offset surfaces (S50) At step S70, the intersection of the vein tree and the blood vessel at the same level (distance level from the outer end of the lung) can be extracted (S90).

The pulmonary blood vessels of the same similar level thus extracted are stored as a set of voxels 30 and serve as a basis for quantitative feature estimation such as diameter and area ratio.

Thus, in this disclosure, at least two embodiments of a method for quantifying blood vessels are disclosed.

In the method of quantifying blood vessels according to an embodiment, a divided pulmonary vein tree is generated, a skeleton of a divided pulmonary vein tree is formed, and then a branch level is determined to extract pulmonary blood vessels in a region of interest, do.

In another method of quantifying blood vessels according to another embodiment, a divided pulmonary vein tree is generated, and offset surfaces of the lung are formed. Then, the intersection points of the separated pulmonary vein tree and the offset surface are extracted, Pulmonary blood vessels are extracted and length information is evaluated.

Both embodiments of the method for quantifying blood vessels provide quantified indicators of pulmonary blood vessels, and in some cases both of these methods may be used in combination to more effectively and accurately assess the circulatory status of the pulmonary vessels.

Hereinafter, the generation of the separated pulmonary vein tree will be described first, and the two methods of determining and quantifying the level of the pulmonary blood vessel will be described.

FIG. 2 is a view for explaining an example of an overall process of generating a divided pulmonary vein tree. 3 is a view showing an example of a pulmonary vein tree classified.

In Fig. 2, from the left to the right, a point set extraction (first from left) extracted as a set of points from volumetric chest CT images, an initial tree construction generated by construction energy minimization method, (Second from left), cutting the proximal region from the initial tree (third from the left), and automatically splitting the children branches of the initial tree into sub-trees branches from left to right), branches from the branches of the initial tree to the truncated root regions, and reconstructs the pulmonary arteries and the pulmonary arteries based on the reassembled initial tree (tree reconstruction and merging (Artery and vein selection; sixth from left).

In FIG. 3, the left side is a coronal view, the right side is a sagittal view, the red color represents the pulmonary artery, and the blue color represents the pulmonary vein.

Hereinafter, a method of generating a pulmonary vascular tree as shown in Figs. 2 and 3 will be described using mathematical equations.

In order to quantify small pulmonary arteries and pulmonary veins, it is desirable to divide them first. Hereinafter, the pulmonary blood vessel classification method will be briefly described.

= {c | c= (i, j, k), i=1,···, nx, j=1,···, ny, k=1,···, nz}을 CT scans으로부터 구성된 복셀들의 집합(set)이라 하고, I(c)를 복셀 c의 attenuation 인텐시티라 하자. 먼저 vascular points V={vi}⊂R³ 가 추출된다. 여기서 v(c)=(x, y, z)^T = ((ci-0.5)×dx,(cj-0.5)×dy,(ck-0.5)×dz)^T는 대응하는 복셀 c의 중심 위치이다. 그러면 종류가 다른 복셀들은 초기 tree T=(V, E)을 구성함으로써 구분될 수 있다. 여기서 E는 가장자리의 집합(set of edges)이다. 상기 초기 트리(tree)는 아래의 방정식(1)에 의해 정의되는 minimizing the cost 방식에 의해 구성된다.

= {c | c = (i, j, k ), i = 1, ···, nx, j = 1, ···, ny, k = 1, ···, nz} the set of voxel constructed from CT scans (set ), And let I (c) be the attenuation intensities of the voxel c. First, the vascular points V = {vi} ⊂R ³ Is extracted. (X, y, z) ^T = (ci-0.5) x dx, (cj-0.5) x dy, (ck-0.5) x dz) ^T is the center position of the corresponding voxel c . Voxels of different kinds can then be distinguished by constructing the initial tree T = (V, E). Where E is the set of edges. The initial tree is constructed by a minimizing the cost method defined by the following equation (1).

(1)

(One)

Where w _j is the weight of vertex j, e _ij is the direction weight of edge (i, j), and α, β, γ ∈ R are positive user-defined constants. w _j is a value indicating the connection characteristic of the vertex j,

. I (v _j ) is the attenuation intensity of v _j normalized by the total vascular points, and Φ (v _j ) is the normalized distance from the vessel boundaries. e _ij is an element representing the directional similarity between the direction of the edge and the vascular orientation evaluated at v _j . The solution minimizing equation (1) is naturally the minimum spanning tree (MST).

After constructing the initial tree, cut the mediastinal region. By grouping only connected vertices, the branches are separated from each other to form a subtree automatically.

Let T _i = (Vi, E i) ⊂ T be the i-th sub-tree of T. Prior to cropping (Livny et al, "Automatic reconstruction of tree skeletal structures from point clouds ", ACM Transactions on Graphics , vol. The orientation vectors {o _i } of all the vertices are re-evaluated by an overall optimization that minimizes the following equation (2) derived from Eq. (29 (6), Article 151,

(2)

(2)

V ^P _i is The parent vertex of vi. Using {oi}, the groups are propagated again from each root vertex to the truncated region, and recombine if there are no overlapping branches. Finally, based on the recombined pulmonary vascular tree, types of vessels (arteries or veins) are determined by the user interface and a pulmonary vascular tree is created. The separated pulmonary vein tree is stored as T _A , T _V , respectively, for the next step.

As described above, the length information of the blood vessel including the pulmonary diameter is quantified based on the separated pulmonary vein tree, and the level of the pulmonary blood vessel is first determined for quantification. Describe a method of using the branching level of the pulmonary blood vessel among the methods of determining the level of the pulmonary blood vessel.

4 is a view for explaining an example of a result of an algorithm for obtaining a skeleton of a distinguished pulmonary vein tree and extracting a node.

In order to determine the branch level, a skeleton of the separated pulmonary vein tree is obtained (S40 in FIG. 1), and a node is extracted (S60 in FIG. 1).

Obtaining the skeletal structure of the pulmonary vessels with a given geometric calculation is a massive task. a method has been disclosed that provides an automated method of extracting a medial line by calculating an outward flux (Bouix et al., "Flux-driven automatic centerline extraction ", Medical Image Analysis , vol. 9, pp. 209-221, 2005). In addition, a curve skeleton construction algorithm using a gradient vector flow has been disclosed (Hassouna et al., "Variational curves skeletons using gradient vector flow ", IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 31, no. 12, pp. 2257-2274, 2009). In addition, the method for surface reconstruction by extracting a skeleton curve from a point cloud comprising a large independent dots and medical applications has been disclosed (Tagliasacchi et al., "Curve skeleton form incomplete extraction point cloud", ACM Transactions on Graphics , vol. 28 (4), Article 71, 2009). There is also, it disclosed a method which is successfully reconstruct the surface from the noise with the non-organized point cloud by introducing the concept snakes arterial (Li et al., "Analysis, reconstruction and manipulation using arterial snakes", ACM Transactions on Graphics , Vol. 29 (6), Article 152, 2010).

 In this embodiment, the skeleton of the tree can be obtained by a simpler method than the method of obtaining the skeleton described above. Because the present disclosure does not originate from raw data such as point clouds or voxels since the skeleton is derived using the above-described segmented pulmonary vein tree.

Specifically, in this embodiment, instead of computing smooth curve skeletons to find a skeleton, we look for branching positions. For example, a medial line is extracted from the above-described divided pulmonary vein tree (upper image in FIG. 4), and node points to which two or more child edges are connected are searched (in FIG. 4, Drawing). A medial line (also called skeleton in this disclosure) of a region, such as a cylindrical shape, is defined by parallel thinning algorithms (Bertrand et al., "A parallel thinning algorithm for medial surfaces ", Pattern Recognition Letters , vol. 16 (9), pp. 979-986, 1995). ≪ / RTI >

Before cutting the proximal region (mediastinal region) in the process of generating the separated pulmonary vascular tree described above, all the vertices, v ∈ V, and of the MST were assigned inverse height values as shown in the following equation (3).

(3)

(3)

인 것들을 critical node로 둔다. 여기서 critical node의 v에 대한 accumulated inverse height는 최대값이다. V^* _C을 제외한 다른 children nodes는 non-critical nodes로 둔다. non-critical nodes를 제거함으로써 골격 구조(skeletal structure)가 추출될 수 있다.Here, v _c is a child node of v, and the child nodes C (v) = {v _c } of some non-leaf node v ∈ V

As critical nodes. Where the accumulated inverse height for critical node v is the maximum value. Other children nodes except V ^* _C are left with non-critical nodes. The skeletal structure can be extracted by removing non-critical nodes.

For example, remove non-critical nodes from leaf nodes that meet any of the following conditions:

a) Leaf node;

b) H (vc)?? |? (v) | : whose path length is shorter than the scaled radius of the parent node (? |? (v) |); or

c) H (vc) <β: whose path length is shorter than a user-defined threshold (β).

In this embodiment,? = 1.2 and? = 3.0 mm are used. When the node is removed, the connected child critical nodes and edges are also deleted and the remaining connected path becomes the skeleton (lower right picture in Fig. 4).

Fig. 4 shows the result of the algorithm of removing the node and obtaining the skeleton in this manner. It does not guarantee that the algorithm provides results linked to single line segments in a mediastinal region with a non-cylindrical shape dependent on parameters a and. However, the algorithm is suitable for the purposes of this disclosure to quantify the morphological characteristics of small blood vessels.

In order to evaluate the radius of the branches in the following process, it is necessary to collect vascular points having the same index of one group (S80 in FIG. 1). To this end, the algorithm assigns an ordered pair (i, j) to the j-th branch of the i-th sub-tree and assigns an ordered pair to its connection points whenever branches of the skeleton branch.

5 is a view for explaining an example of a method of quantifying a blood vessel using a node according to an embodiment.

Using the above ordered pairs, the k-nearest boundary points Nb (v _i ) = {q _j | j = 1, ... , k} ⊂ ∂V are collected in the same group at each node v _i of the pulmonary vein tree T Whereby small pulmonary arteries and pulmonary veins of the same level corresponding to the region of interest can be extracted as points of voxels.

을 만족하며, δ은 사용자 정의 파라미터이다. 도 5(a)에서 노드 v_i(201)에 연결된 회색점들(205)은 N_b(v_i)을 보여준다. 본 예의 실험에서는 k=26, δ=π/4를 사용한다.Here, the points gathered in the same group

And? Is a user-defined parameter. Gray points 205 connected to node v _i 201 in FIG. 5 (a) show N _b (v _i ). In the experiment of this example, k = 26 and? =? / 4 are used.

A separate pulmonary vascular tree T is used to collect spatially adjacent points in the voxel structure. We can obtain the radius of the branch at each node by fitting the cylinder with N _b (v _i ) using the simple least-squares method as shown in Figure 5 (b) (D.Eberly, "Fitting 3D data with a cylinder ", [Online] Feb. 2003. Available: http://www.geometrictools.com/Documentation/CylinderFitting.pdf (URL).

이 반경으로 사용된다. 이웃한 점들이 없을 때에는 그 노드의 반경을 CT 해상도의 절반 사이즈로 두고 계산한다. 예를 들어, CT 이미지의 해상도가 약 0.545mm 내지 0.693mm이기 때문에 반경 평가 작업에서 사용되는 데이터를 위해 rmin = 0.3mm로 둔다.When the original blood vessel is too thin and the neighbor points of a node are less than 10 and insufficient, the following equation (4)

This radius is used. When there are no neighboring points, the radius of the node is calculated to be half the size of the CT resolution. For example, since the resolution of the CT image is about 0.545 mm to 0.693 mm, rmin = 0.3 mm is reserved for the data used in the radial evaluation work.

(4)

(4)

6 is a view for explaining an example of a method of quantifying a blood vessel using an offset surface according to another embodiment.

FIG. 6 is described below in detail in FIGS. 7 to 12. FIG.

7 is a view for explaining an example of a method of generating an Euclidean distance field for forming an offset surface.

 As a method for determining the level of small pulmonary blood vessels for quantification of pulmonary blood vessels, different from the method of determining the pulmonary vein level (method using the shape of blood vessels) described above, To evaluate the length information.

According to the fact that the pulmonary vein extends from the interior to the distal end of the body, it can be assumed that there are similar sized blood vessels at the same distance from the outer end boundary surfaces of the lung. Therefore, the intersection between the vessel and the inner surface of the lung, which is a distance from the outer end boundary surfaces of the lung, except for the mediastinal region, is found, and the diameter of the vessel at these intersections is evaluated .

In order to obtain the intersection points, it is necessary to first gradually extract inner surfaces (see Fig. 6 (a)). The inner surface at a distance from the outer end boundary surface of the lung becomes offset surfaces at that distance. Offset surfaces can be created using a more time-efficient surface data calculation method than volume-based methods, such as face-based offset or vertex-based offset.

However, the surface data calculation schemes are vulnerable to frequent local and global interference when offsetting the surface of the lungs inward. Particularly in the case of this embodiment, the offset distances are 5 mm to 30 mm, which is much larger than the length of the surface extracted by the marching cubes from CT images, and interference is difficult to avoid. Therefore, in this embodiment, an offset surface is created by a volume-based method of generating an Euclidean distance field (see FIG. 7 (a)).

In the process of generating the above-described segmented pulmonary vascular tree, the right and left lungs are clearly divided into LR, LL ⊂Γ before blood vessels are extracted into voxels (Hu et al., "Automatic lung segmentation for accurate quantitation of volumetric X- ray CT images &quot;, IEEE Transactions on Medical Imaging , vol. 20 (6), pp. 490-498, 2001).

For the LR and LL, an Euclidean distance field is generated from these boundaries, and iso-surfaces are extracted from the required offset distances do (see FIG. 7 (b)). Here, do ∈ {5, 10, 15, 25, 30} in the millimeter unit was used in the experiment of this embodiment (see FIG. 6 (a)).

For computational efficiency, an octree structure is used to generate the Euclidean distance field where the voxels are at the finest level of the octree, as shown in FIG. 7 (a). When a high spatial resolution is required only in the partial area, the octree becomes a good data structure.

For example, if dmin (c, ∂L) <do <dmax (c, ∂L) is satisfied, and only if it satisfies, one cell is refined to eight children from the root cell of the octree. Here, dmin (c, ∂L) and dmax (c, ∂L) are the minimum and maximum distances in 8-corners of c from ∂L and the boundary of L = LR ∪ LL. Let the result be voxels S _R (do) ⊂LR and S _L (do) ⊂LL in the left and right lungs satisfying dmin (c, ∂L) <do <dmax (c, ∂L), respectively. Using Octree Details of implementation for generating a distance field is paper (Frisken et al,. "Adaptively sampled distance fields: A general representation of shape for computer graphics", Proceedings of ACM SIGGRAPH , pp. 249, 254, 2000).

The iso-surface is extracted from the Euclidean distance field described above in the form of a triangular mesh (see Fig. 7 (b)). The triangular mesh is computed using a known marching cubes algorithm, which can be performed in a time-efficient manner by parallel computing using graphics processing units (GPUs).

8 is a view for explaining an example of a process of extracting an intersection point between an inner surface (an inner surface) and a pulmonary blood vessel.

As described above, the iso-surface is extracted as a triangular mesh to obtain the medial surface, or offset surfaces, at specific distances and then find the intersections between the offset surface and the small pulmonary vessels. To this end, the offset surface is calculated as the surface voxel S extracted as shown in FIG. 8 (see FIG. 8 (a)). The method of extracting the skeleton of the separated pulmonary vascular tree has been described above. 8 (b), 6 (c), 8 (b), 8 (c) and 8 (c) show the bit-mask of the pulmonary veins as a skeleton of the tree bit- d)). Thus, the intersection point is obtained by simply checking the intersection between the surface voxel S and the skeleton of the bit-wised tree in Γ (see FIG. 6 (d) and FIG. 8 (d)).

FIG. 9 is a diagram illustrating an example of a process of extracting a mono-oriented region for segmentation of lungs using a 2D schematic diagram.

Since this example is of interest to the distal regions of the lung (see FIG. 9 (a)), it is necessary to avoid measuring the length of the pulmonary blood vessel (eg, diameter) in the mediastinal region . A mono-oriented region partition can be applied to find areas to avoid length measurement.

For example, active contours (see Fig. 9 (c)) are generated from the local maximum points of the outward distance field (see Fig. 9 (b)) from ∂L. The active contours are routed to neighboring neighboring cells at the same speed in a manner that maintains the same distance until collision with other active contours or ∂L. The outer regions Γ / L can be partitioned into the same local maximum number by collecting the cells propagated from the same velocity point. The result of this algorithm is Voronoi diagram of ∂L.

Most of the lung's mediastinal region includes the thoracic cavity (encompass). Therefore, the region included in the root region is a blue region (its seed points are corners of Γ), a green region (including boundary cells of Γ) and a crossing region (this is a simple thresholding (See FIG. 9 (d) and FIG. 9 (e)) by removing the region including the ribs or vertebrae which can be easily divided by the region of the bone.

10 is a view for explaining an example of root region extraction applied to volumetric CT images.

The octree is also used in this example to be effective for the computational efficiency and the small feature of the lung shape. Figure 10 shows the result of down-sampled resolution from 512x512x512 original images to 128x128x128 for computer computational efficiency. The boundary surface of the lungs and mediastinal region is a ∂L∩M, S _R When the scored (d ⁰⁾ and S _L (d ⁰⁾ can be calculated distance (distance) from the ∂L / (∂L∩M) .

FIG. 11 is a view for explaining an example of surfels calculation, and FIG. 12 is a view for explaining an example of surfels calculated by 3D rendering.

The defense vector is evaluated by performing basic component analysis on small blood vessels, as well as performing overall optimization by the aforementioned equation (2) before constructing the initial tree T. [ The result of the evaluation of the defense vector is stored as surface elements called surfels. The surfels can be viewed as a circular disk with a normal vector and radius at the offset surface.

The region unnecessary for quantification is removed in order to evaluate the length information (e.g., diameter) of small pulmonary blood vessels at the intersection of the offset surface and the pulmonary vein tree separated (refer to FIG. 8 and FIG. 11 (a) 10 description).

However, since the branch of the divided pulmonary vein tree does not always vertically pass through the offset surface, in order to obtain the diameter or area of branches of the pulmonary vein tree, the branch of the divided pulmonary vein tree is projected perpendicular to the offset surface The offset area which is the cross-sectional area of the blood vessel on the offset surface can be calculated (see Fig. 11 (b)).

Since the surfels reflect the orientation of branches of the divided pulmonary vein tree, the radius of the small pulmonary vein can be evaluated in a direction perpendicular to the orientation of branches of the pulmonary vein tree using the offset area and the normal vector of surfels .

Using the small radius of the pulmonary vein evaluated, the mean area of the small pulmonary artery and pulmonary vein can be calculated and also the cross-sectional area between the small pulmonary vein and the offset surface at the offset surface can be measured (see FIG. 12).

Non-contrast volumetric chest CT scans with a thickness of less than 25 mm (0.545 mm to 0.693 mm) in 25 COPD patients were used for the blood vessel quantification method according to this example. The algorithm used in the experiment does not depend on the respiratory phase but uses enough images for consistent comparison.

In the classification of pulmonary blood vessels, the method of quantifying blood vessels according to the present disclosure was evaluated by a mathematical virtual model and a study of the preceding experts (Park et al., "Automatic classification of pulmonary artery and vein by tree reconstruction at volumetric chest CT ", Submitted , 2013).

The average score values for each segment are shown in Table 1.

Since the device program for parallel computing using GPUs is written in nVidia CUDA 5.0 SDK, the host program is Microsoft Visual C ++ (10.0.0), since the experiment was performed in a software platform developed for performing the blood vessel quantification method according to the present example. ). The test environment for this experiment is a desktop PC consisting of nVidiaQuadro 600 (1GB), 12GB main memory and Intel Core i7 960 (3.2GHz) for parallel computing.

Figure 13 is a diagram illustrating the accuracy of radius estimation for virtual vascular phantom models.

A virtual model is created to measure the accuracy of the calculation of the vessel radius. FIG. 13 (a) shows a single vascular tree mode aligned to a global coordinate, and FIG. 13 (b) shows an assembled vascular phantom model. The radial evaluation error was 0.101 ± 0.042 mm (mean ± SD) at 70 sampled positions for the single model and 0.137 ± 0.055 mm (mean ± SD) at 140 points for the coupled model (see FIG. This seems acceptable.

Table 1 shows the properties evaluated at the branching nodes from the vascular skeleton, and Table 2 shows the results of gradual peeling.

In the above experiment, as shown in Tables 1 and 2, the diameter of the pulmonary vein tends to be larger than the diameter of the pulmonary artery. In Table 2, although the average calculated number of veins is smaller than the number of arteries, the area percentages are nearly equal to each other. Percentage of area occupied by the pulmonary veins at the lung surface tends to increase in both arteries and veins, up to 30 mm medially.

Morphological properties according to branching level Branch level Artery Vein NO * MEAN_D STD_D NO MEAN_D STD_D 1 ^st 630.20 1.895 0.112 601.72 1.912 0.220 2 ^nd 413.17 1.956 0.131 402.29 2.014 0.312 3 ^rd 305.39 2.135 0.133 299.32 2.251 0.223 4 ^th 242.18 2.412 0.142 220.13 2.481 0.412

Where NO is the total number of vessels, MEAN_D is the mean of diameters, and STD_D is the standard deviation of diameters.

In the case of gradual peeling, Properties Offset Level d ^o (mm) 5 10 15 20 25 30 Artery NO 929.06 857.00 735.71 678.70 573.24 454.35 MEAN_D (mm) 1.541 1.835 1.921 1.975 1.975 2.073 STD_D 0.213 0.113 0.121 0.131 0.125 0.136 W_AREA * (mm ² ) 2.241 3.462 3.507 3.772 3.682 4.050 CR_AREA (mm ² ) 2.780 3.771 3.891 4.013 4.152 4.413 % 1.98 2.30 3.21 3.75 4.10 4.45 Vein NO 779.47 794.29 650.82 574.29 503.18 422.38 MEAN_D (mm) 1.761 1.882 1.975 2.063 2.073 2.132 STD_D 0.979 0.201 0.206 0.270 0.306 0.800 W_AREA (mm ² ) 2.931 3.615 3.676 4.151 4.429 4.462 CR_AREA (mm ² ) 3.316 3.914 4.014 4.443 4.913 4.902 % 1.98 2.66 2.93 3.28 3.83 4.52

Where W_AREA is the weighted average area, CR_AREA is the mean of the cross sectional area between vessels and inner surfaces, and% is the occupied area percentage of the vessels (= CR_AREA x NO / area of S (d ^o )) .

Figure 14 is box plots showing the number of vessels, mean diameter, and area ratio occupied by the vessels by gradual peeling and tree branching levels.

14, the red box is located between a quartile and the median, the green boxis is located between the median and a third quartile, and the yellow points are mean values.

The statistical significance in Figure 14 should be compared with the clinical parameters, but morphological trends can be observed in these graphs. For example, the rate of increase in diameter gradually becomes gradual at a steep slope as it progresses toward the proximal region by the gradual peeling method. On the other hand, the slope of the average radius changes almost linearly along the branches of the tree up to the fourth level.

Various embodiments of the present disclosure will be described below.

(1) The step of quantifying the length information of the blood vessel comprises the steps of: calculating the radius of the blood vessel using the found voxels; And calculating the area ratio of the blood vessel to the region of interest using the calculated radius of the blood vessel.

The length information of the blood vessel may mean not only the radius and diameter of the blood vessel but also the width or thickness of the blood vessel which is difficult to be expressed by the diameter or the radius. In some cases, it means a vascular length perpendicular to the diameter direction of the blood vessel It is possible. Once the radius of the blood vessel is determined, quantitative values such as the cross-sectional area of the blood vessel and the ratio of the cross-sectional area of the blood vessel on the specific offset surface of the organ can be extracted.

(2) the step of finding the voxels of the blood vessel comprises: determining the level of the blood vessel according to the morphological characteristic of the blood vessel or the position in the organ; And finding voxels of a blood vessel at a level selected as a region of interest.

(3) The steps of finding the voxels of the blood vessel include: a process in which the medial line of the extracted blood vessel is extracted; The process of finding a voxel on the center line included in the region of interest; And a process of finding neighboring boundary voxels in a voxel on the center line.

(4) The steps of finding the voxels of the blood vessel include: a process of extracting a center line of a blood vessel and generating a skeleton of the blood vessel; A process in which a node where a blood vessel branches is extracted using a skeleton of an artery; And a process of finding voxels within a spatially spatially spatially selected region of a node selected as a region of interest among the extracted nodes.

(5) The step of finding the voxels of the blood vessel comprises the steps of generating an offset surface defined as a set of voxels at a certain distance inward from the outer surface of the organ; And a process of finding voxels corresponding to an intersection of the extracted blood vessel and the offset surface.

(6) The step of quantifying the length information of the blood vessel includes: a process in which the found voxels are cylinder-fitted; And calculating the radius of the blood vessel at the node using the cylinder-fitted voxels.

(7) the step of quantifying the length information of the blood vessel comprises the steps of: calculating an offset area formed on the offset surface by the voxels corresponding to the intersection; And calculating a radius of the blood vessel in a direction perpendicular to the direction vector of the blood vessel using a surface normal vector of the offset surface, a vascular orientation vector and an offset area, The method comprising the steps of:

(8) The step of extracting blood vessels includes: a process in which a lung image is acquired; A process in which a minimum spanning tree method is applied to the pulmonary blood vessels included in the lung image to generate an initial pulmonary vein tree; A process in which an initial pulmonary vein tree is removed from the initial pulmonary vein tree and the initial pulmonary vein tree is automatically separated into sub-trees; The process of merging the subtrees is extended to the root region from which the pulmonary veins of the initial tree have been removed; And a process in which a pulmonary vein of the recombined initial tree is classified into a pulmonary artery and a pulmonary vein and a pulmonary vascular tree is generated.

(9) The steps in which voxels are found in the blood vessels include: the process of generating a pulmonary skeleton and a pulmonary vein skeleton using a segmented pulmonary vein tree; A process of extracting a node where the pulmonary branch branches based on the pulmonary artery skeleton and the pulmonary vein skeleton; And neighboring boundary voxels are found at a node selected as a region of interest of the lung among the extracted nodes, wherein the step of quantifying the length information of the blood vessel comprises the steps of: fitting process; And calculating the radius of the blood vessel at the node using the cylinder-fitted voxels.

(10) The step of finding the voxels of the blood vessel comprises: generating an offset surface defined as a set of voxels at a certain distance inward from the outer surface of the lung; And a step of finding voxels corresponding to the intersection of the separated pulmonary vein tree and the offset surface. The step of quantifying the length information of the blood vessel comprises: calculating an offset area formed by the voxels corresponding to the intersection on the offset surface process; And calculating a radius of the pulmonary blood vessel in a direction perpendicular to the direction vector of the pulmonary blood vessel using a surface normal vector of the offset surface, a vascular orientation vector of the pulmonary vein, and an offset area Wherein the method comprises the steps of:

(11) The process of extracting a node includes: a process of assigning an ordered pair to a node from the outer end of the lung; and the process of finding the voxels is: a process of selecting a node of a region of interest using an ordered pair; The method comprising the steps of:

(12) The process of finding the voxels corresponding to the intersection is as follows: the process of extracting the medial line of the pulmonary blood vessel as a skeleton of the separated pulmonary vascular tree; The process of extracting the intersection point between the center line and the offset surface; And neighboring boundary voxels are found at an intersection point of the neighboring voxels.

(13) The process of creating the offset surface is as follows: the right lung and the left lung are divided as a set of voxels from the lung image; A process in which an Euclidean distance field is generated from the boundaries of the divided right lung and left lung; And extracting an iso-surface at a required offset distance. &Lt; Desc / Clms Page number 19 &gt;

(14) A computer-readable recording medium recording a program for causing a computer to execute a method for quantifying a blood vessel.

According to the method of quantifying blood vessels according to the present disclosure, various characteristics indicating the distribution and size of the pulmonary arteries and pulmonary veins, for example, the mean radius, the cross-sectional area of the pulmonary vein crossing the inner surface, A quantification method for analyzing the morphological characteristics of pulmonary blood vessels such as area percent is provided.

According to the method of quantifying blood vessels according to the present disclosure, many pulmonary diseases such as pulmonary hypertension, interstitial lung disease and chronic obstructive pulmonary disease (COPD) are classified based on a quantitative approach considering the spatial distribution and size of the automatically classified small pulmonary artery and pulmonary vein It can be evaluated more effectively.

Claims (15)

Extracting a blood vessel as a three-dimensional set of voxels based on a medical image of an organ;
A step of finding voxels of a blood vessel included in a region of interest of a long term; And
And quantifying the length information of the blood vessel including the diameter using the found voxels. The method according to claim 1,
The step of quantifying the length information of the blood vessel comprises:
A process in which the radius of the blood vessel is calculated using the detected voxels; And
And calculating the area ratio of the blood vessel to the region of interest using the calculated radius of the blood vessel. The method according to claim 1,
The steps for finding voxels in an artery are:
A process in which the level of a blood vessel is determined according to a morphological characteristic of the blood vessel or a position within the organ; And
And a process of finding voxels of a blood vessel at a level selected as a region of interest. The method according to claim 1,
The steps for finding voxels in an artery are:
A process in which the medial line of the extracted blood vessel is extracted;
The process of finding a voxel on the center line included in the region of interest; And
And a process of finding neighboring boundary voxels in a voxel on a center line. The method according to claim 1,
The steps for finding voxels in an artery are:
A process of extracting a center line of a blood vessel and generating a skeleton of the blood vessel;
A process in which a node where a blood vessel branches is extracted using a skeleton of an artery; And
And a process of finding voxels within a spatially spatially spatially selected node selected as a region of interest among the extracted nodes. The method according to claim 1,
The steps for finding voxels in an artery are:
A process in which an offset surface is defined that is defined as a set of voxels at a certain distance inward from the outer surface of the organ; And
And a process of finding voxels corresponding to an intersection of the extracted blood vessel and the offset surface. The method of claim 5,
The step of quantifying the length information of the blood vessel comprises:
A process in which the found voxels are cylinder-fitted; And
And calculating the radius of the blood vessel at the node using the cylinder-fitted voxels. The method of claim 6,
The step of quantifying the length information of the blood vessel comprises:
Calculating an offset area formed on the offset surface by the voxels corresponding to the intersection; And
And calculating a radius of the blood vessel in a direction perpendicular to the direction vector of the blood vessel using a surface normal vector of the offset surface, a vascular orientation vector and an offset area of the blood vessel. A method for quantifying blood vessels. The method according to claim 1,
The step of extracting blood vessels comprises:
The process of acquiring a lung image;
A process in which a minimum spanning tree method is applied to the pulmonary blood vessels included in the lung image to generate an initial pulmonary vein tree;
A process in which an initial pulmonary vein tree is removed from the initial pulmonary vein tree and the initial pulmonary vein tree is automatically separated into sub-trees;
The process of merging the subtrees is extended to the root region from which the pulmonary veins of the initial tree have been removed; And
Wherein the pulmonary artery and the pulmonary vein are classified into a pulmonary vein and a pulmonary vein tree. The method of claim 9,
The steps for finding voxels in an artery are:
The process by which the pulmonary artery skeleton and the pulmonary vein skeleton are generated using the separated pulmonary vein tree;
A process of extracting a node where the pulmonary branch branches based on the pulmonary artery skeleton and the pulmonary vein skeleton; And
And a process of finding neighbor boundary voxels in a node selected as a region of interest of the lung among the extracted nodes,
The step of quantifying the length information of the blood vessel comprises:
A process in which the found voxels are cylinder-fitted; And
And calculating the radius of the blood vessel at the node using the cylinder-fitted voxels. The method of claim 9,
The steps for finding voxels in an artery are:
An offset surface is defined that is defined as a set of voxels spaced inwardly from the outer surface of the lung; And
And a process of finding voxels corresponding to the intersection of the separated pulmonary vein tree and the offset surface,
The step of quantifying the length information of the blood vessel comprises:
Calculating an offset area formed on the offset surface by the voxels corresponding to the intersection; And
Calculating a radius of the pulmonary blood vessel in a direction perpendicular to the direction vector of the pulmonary blood vessel using a surface normal vector of the offset surface, a vascular orientation vector of the pulmonary vein, and an offset area The method comprising the steps of: The method of claim 10,
The process of extracting a node is as follows:
And assigning an ordered pair to the node from the outer end of the lung,
The process of finding voxels is:
And selecting a node of a region of interest using an ordered pair. The method of claim 11,
The process of finding the intersection voxels is:
The skeleton of the separated pulmonary vascular tree, in which the medial line of the pulmonary vein is extracted;
The process of extracting the intersection point between the center line and the offset surface; And
And neighboring boundary voxels are found at an intersection point of the neighboring voxels. The method of claim 11,
The process of creating an offset surface is:
A process in which right lung and left lung are divided as a set of voxels from a lung image;
A process in which an Euclidean distance field is generated from the boundaries of the divided right lung and left lung; And
And extracting an iso-surface at a desired offset distance. &Lt; Desc / Clms Page number 20 &gt; A computer-readable recording medium storing a program for causing a computer to execute the method according to any one of claims 1 to 14.

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