Research on a bifurcation location algorithm of a drainage tube based on 3D medical images
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Abstract
Based on patient computerized tomography data, we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model. To improve the efficiency and accuracy of identifying puncture points, a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction. According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal, the proposed algorithm can provide an optimal route for a drainage tube for the hematoma, the precise position of the puncture point, and preoperative planning information, which have considerable instructional significance for clinicians.
Keywords
Multitube drainage tube Bifurcation localization algorithm 3D medical image Path planning Intracranial hematomaAbbreviations
- CT
Computerized tomography
- ROI
Region of interest
Introduction
Trauma can cause blood vessels to burst in the brain or between the skull and brain tissue. Subsequently, an intracranial hematoma may form from blood pooling up in the brain or between the skull and brain, which compresses the brain tissue. An intracranial hematoma is a common but serious secondary damage mode of craniocerebral injury. The incidence of closed craniocerebral injury is approximately 10%, while the incidence of severe craniocerebral injury is approximately 40%–50%. The complications of intracranial hematomas include their effect on cerebral blood flow, cerebral hernia, cerebral edema, and Cushing’s reaction, which can seriously damage brain tissue, and some of these damage modes are irreversible and life-threatening [1, 2, 3, 4]. Puncture and ablation of the intracranial hematoma is a widely used medical method. With the increasingly busy modern life, heavy work stress, mental pressure, and lack of physical labor, the incidence rates show that the age of onset of this condition is decreasing. Once it occurs, the disease places a burden on individuals and families, so all countries and the international community have attached considerable importance to it [2]. The advantages of puncture operation include effective application, high security, quick recovery, short operating time, and relatively few postoperative complications [5, 6, 7]. At present, the methods of positioning such as the stereotactic technique [8] and neuronavigation technique [9] mainly involve a freehand technique for insertion of a drainage tube, which is based on fixed anatomical landmarks, does not consider individual variations, and often exhibits insufficient precision. Hence, a time-efficient and low-cost technique to localize the hematoma puncture point and to provide path planning will be beneficial, especially when highly sophisticated and expensive navigation systems cannot be made available in developing regions. In the present study, according to a dataset of brain computerized tomography (CT) images, we reconstructed the 3D model of a patient’s brain by using a 3D Slicer software [10, 11, 12, 13, 14] and extracted the area of intracranial hematoma as well. To quickly find the best puncture point in theory, an algorithm based on k-means clustering [15] was proposed to optimize the search space, eliminate redundant computation, effectively sort out the search space, and reduce the possibility of particles falling into a local extremum [16]. Then, the algorithm is improved from the viewpoint of the search mode, and the point cloud search algorithm of adaptive weighted particle swarm optimization [17, 18, 19] is proposed, which greatly reduces the calculation time and the number of iterations of the algorithm. In the experimental environment, the algorithm proposed in this study can greatly improve the search efficiency of this method in obtaining the global optimal solution. Compared with the direct optimization algorithm [20], the average maximum acceleration ratio is 1288.67%. According to the characteristics of the multitube drainage tube [21] and the clinical needs of the puncture process for intracranial hematoma removal, the bifurcation location algorithm provides the precise position of the puncture point, the optimum location of probes, and the best route under ideal conditions. In this study, the 3D model is effectively combined with the multitube drainage tube; furthermore, a preoperative simulation is proposed, which can provide significant guidance and value in formulating the puncture operation plan and decreasing the risk of blindness.
Segmentation of the intracranial hematoma
In clinical medicine, for patients with an intracranial hematoma, most hospitals conduct CT, hematoma location, puncture marking, followed by other processes [5, 6]. We used the 3D Slicer software to implement three-dimensional reconstruction based on brain CT data. This approach can greatly reduce the treatment time and improve efficiency. 3D Slicer software can reconstruct anatomical parts such as the hematoma, blood vessels, skull, and nerve bundles. In this way, doctors can avoid important parts such as blood vessels and nerve bundles when they mark the puncture points and accurately and efficiently puncture the hematoma, thus reducing treatment time [12, 13, 14]. The 3D Slicer program is a software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. The platform originated in an MSc thesis program in 1998 and is jointly sponsored by the Surgery Plan Laboratory at Brigham and Women’s Hospital and the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology [10, 11, 12].
Hematoma modeling
After the CT data have been preprocessed, three-dimensional reconstruction of the brain can be realized to obtain a three-dimensional image. In 3D Slicer, the region of interest (ROI) function is used to extract the hematoma area in the image, and the Segmentations module is selected to model the hematoma, providing models in the STL and OBJ formats to extract the “optimal external central axis” needed in this study.
Hematoma location locking
Applying the threshold method to segment the hematoma
Methods
Basic concept and definition
As an effective tool to describe the morphological characteristics of the geometry, the center axis has many advantages; for example, it can describe the geometry information inside the model and at any position of the boundary [22, 23, 24]. Furthermore, it provides a method to intuitively describe the geometric characteristics of a complex model and represent the topological relation of the model. The definition of the external central axis is as follows: Let Ω be a model in n-dimensional Euclidean space R^{n}, and assume it is homeomorphic to a 3D closed sphere; we define Ω as an n − D model. The external center axis of Ω is the set of the centers of the maximum circumscribed spheres [25]. Inspired by the concept of the external center axis, we found the optimal external center axis suitable for this study. The maximum Euclidean distance in the hematoma point cloud is determined, and the line segment joining the two points is defined as the optimal external center axis.
Algorithm of optimal external central axis extraction
The optimal external central axis can be obtained using a direct optimization algorithm [20]. However, a large amount of computation is required because the time complexity increases at the N^{2} level with an increasing quantity of point clouds. In the case of a large-scale geometric model, the approach to obtain the central axis is excessively time consuming. Therefore, to reduce the computation redundancy, a point cloud clustering algorithm based on k-means is proposed to optimize the search space [15, 16]. The algorithm presents strong adaptive ability, robustness, and low computation requirements. However, it does not consider the search method, and thus, it is not a suitable method for extracting the axis. Based on the above analysis, we propose the point cloud search arithmetic method for modified adaptive weighted particle swarm optimization [17, 18, 19, 26, 27, 28, 29, 30]. The algorithm reduces computational redundancy and greatly enhances the efficiency of searching axes in complex geometries. Our proposed algorithm is written in MATLAB (The MathWorks, Inc., MA).
Algorithm flow
The flow of the point cloud search arithmetic method for modified adaptive weighted particle swarm optimization based on k-means clustering is as follows:
Step 1. First, define the data set D in 3D Euclidean space and randomly select n items (n is the number of selected clusters) as centroids C_{i}(i = 1, 2, ⋯, n), where each centroid is the clustering center of a category. Then, calculate the Euclidean distance between C_{i} and D_{j}(1, 2, ⋯, k) in D. For example, if D_{j} is the nearest to C_{i}, it is classified as cluster i.
Step 2. Through the first step, the data set D is initially divided into k classes. Calculate the mean value of each dimension of all data items in each cluster. A new centroid is formed and updated to the new centroid.
Step 3. Repeat steps 1 and 2 to obtain the new centroid until the centroid of each class no longer changes.
Step 4. Randomly initialize the particles in the solution space.
Repeat step 5 and incrementally iterate until the optimal position of the given threshold is found.
Note: \( {v}_{id}^k \) is the d − th dimensional component of the k − th iterative particle i flight velocity vector; \( {x}_{id}^k \) is the d − th dimensional component of the position vector; c_{1}c_{2} is the maximum learning step parameter; r_{1}r_{2} is the random function in the range of [0 − 1]; and ω is the inertia weight of the search, which has a considerable impact on the search scope and method.
Step 6. Compute the mean distance between d_{1}, d_{2}, ⋯, d_{n}: \( {D}_{EXP}=\frac{2\ast \sum {D}_{ij}}{k\ast \left(k-1\right)} \), where \( {D}_{ij}=\sqrt{{\left({x}_i-{x}_j\right)}^2+{\left({y}_i-{y}_j\right)}^2+{\left({z}_i-{z}_j\right)}^2} \) denotes the distance between two clustering centers.
Step 7. Calculate and obtain the difference between D_{ij} and D_{EXP}: S_{ij} = D_{ij} − D_{EXP}, where ω_{ij} denotes the ratio of S_{ij} to ∑S_{ij} as the generating weight of the small block ω_{ij}: \( {\omega}_{ij}=\frac{S_{ij}}{\sum {S}_{ij}} \).
Optimal path planning
Notation
Symbol | Description |
---|---|
M_{1}(x_{1}, y_{1}, z_{1}), M_{2}(x_{2}, y_{2}, z_{2}) | The coordinate of the joint point of the optimal external axis and the boundary of the hematoma |
d_{12} = ∣ M_{1}M_{2}∣ | The distance from M_{1} to M_{2} |
d_{34} = ∣ M_{3}M_{4}∣ | The maximum diameter of the vertical surface through M_{1/2} in the hematoma |
M_{3}(x_{3}, y_{3}, z_{3}), M_{4}(x_{4}, y_{4}, z_{4}) | The coordinates of the endpoints of d_{34} in the hematoma |
D, d | Ddenotes the diameter of the duct in the main tube; d denotes the diameter of the hole in the subtube |
d_{O} | The distance between the two central points of the holes |
d_{B} | The distance between the two bifurcations |
\( {d}_{B_i} \) | The distance between the main probe tip and the i − th bifurcation-tube probe tip (we define the first subtube to be nearest to the main probe tip) |
The known conditions can be determined by M_{1} and M_{2}
According to the intersection coordinates M_{1}(x_{1}, y_{1}, z_{1}) and M_{2}(x_{2}, y_{2}, z_{2}), we obtain the parameter equation of the center axis as follows:
\( \Big\{{\displaystyle \begin{array}{c}x={x}_1+\left({x}_2-{x}_1\right)\ t\\ {}y={y}_1+\left({y}_2-{y}_1\right)\ t\\ {}z={z}_1+\left({z}_2-{z}_1\right)\ t\end{array}},\mathrm{where}\ 0\le t\le 1 \) (6)
The midpoint coordinates of M_{1}M_{2} are \( {M}_{1/2}=\left(\frac{x_1+{x}_2}{2},\frac{y_1+{y}_2}{2},\frac{z_1+{z}_2}{2}\right) \)_{.}
The distance from M_{1} to M_{2} is \( {d}_{12}=\sqrt{{\left({x}_2-{x}_1\right)}^2+{\left({y}_2-{y}_1\right)}^2+{\left({z}_2-{z}_1\right)}^2} \)_{.}
The distance from M_{3} to M_{4} is \( {d}_{34}=\sqrt{{\left({x}_4-{x}_3\right)}^2+{\left({y}_4-{y}_3\right)}^2+{\left({z}_4-{z}_3\right)}^2} \)_{.}
Optimal path planning of two subtube drainage tubes
According to the clinical demand, the best position of the drainage tube in the hematoma is the midpoint between the two holes, coinciding with the midpoint of the central axis, that is, \( {M}_{1/2}=\left(\frac{x_1+{x}_2}{2},\frac{y_1+{y}_2}{2},\frac{z_1+{z}_2}{2}\right) \), and the path of the main drainage tube coincides with the optimal external center axis.
Calculating the coordinates of B_{1} and B_{2}
Substituting t_{1}, t_{2} into Eq. (6), we obtain the coordinates of \( {B}_1\left({x}_{B_1},{y}_{B_1},{z}_{B_1}\right) \) and \( {B}_2\left({x}_{B_2},{y}_{B_2},{z}_{B_2}\right) \)_{.}
Calculating the direction vector of the subtube
Substituting t_{31}, t_{32} into Eq. (6), we obtain the coordinates of M_{31}, M_{32}; the direction vector of subtubes are \( \overrightarrow{M_{31}{M}_3}=\left({m}_{31},{n}_{31},{k}_{31}\right) \) and \( \overrightarrow{M_3{M}_{32}}=\left({m}_{32},{n}_{32},{k}_{32}\right) \), and the parameter equation of the two subtubes is as follows:
Scenario one:
\( \left\{{\displaystyle \begin{array}{c}x={x}_{B_1}+{m}_{31}l\\ {}y={y}_{B_1}+{m}_{31}l\\ {}z={z}_{B_1}+{m}_{31}l\end{array}},\right\{{\displaystyle \begin{array}{c}x={x}_{B_2}+{m}_{32}l\\ {}y={y}_{B_2}+{m}_{32}l\\ {}z={z}_{B_2}+{m}_{32}l\end{array}} \) (12)
Scenario two:
\( \left\{{\displaystyle \begin{array}{c}x={x}_{B_1}+{m}_{32}l\\ {}y={y}_{B_1}+{m}_{32}l\\ {}z={z}_{B_1}+{m}_{32}l\end{array}},\right\{{\displaystyle \begin{array}{c}x={x}_{B_2}+{m}_{31}l\\ {}y={y}_{B_2}+{m}_{31}l\\ {}z={z}_{B_2}+{m}_{31}l\end{array}},\left(\mathrm{where}\ l\ge 0\right) \) (13)
In the first scenario, the coordinates of the probe tip of the subtube are as follows:
\( {B}_1^F=\left(\frac{x_{B_1}+{x}_{B_{11}}}{2},\frac{y_{B_1}+{y}_{B_{11}}}{2},\frac{z_{B_1}+{z}_{B_{11}}}{2}\right),{B}_2^F=\left(\frac{x_{B_2}+{x}_{B_{21}}}{2},\frac{y_{B_2}+{y}_{B_{21}}}{2},\frac{z_{B_2}+{z}_{B_{21}}}{2}\right) \) (15)
In the second scenario, the coordinates of the probe tip of the subtube are.
\( {B}_1^F=\left(\frac{x_{B_1}+{x}_{B_{12}}}{2},\frac{y_{B_1}+{y}_{B_{12}}}{2},\frac{z_{B_1}+{z}_{B_{12}}}{2}\right),{B}_2^F=\left(\frac{x_{B_2}+{x}_{B_{22}}}{2},\frac{y_{B_2}+{y}_{B_{22}}}{2},\frac{z_{B_2}+{z}_{B_{22}}}{2}\right) \) (16)
The optimal path planning of three subtube drainage tubes
According to the clinical demand, the best position of the drainage tube in the hematoma is the midpoint of the second hole, coinciding with the midpoint of the central axis, i.e., \( {M}_{1/2}=\left(\frac{x_1+{x}_2}{2},\frac{y_1+{y}_2}{2},\frac{z_1+{z}_2}{2}\right) \), with the path of the main drainage tube coinciding with the optimal external center axis.
Calculating the coordinate of B_{1}, B_{2}, and B_{3}
Substituting t_{11}, t_{12} and t_{13} into Eq. (6), we obtain the coordinates \( {B}_1\left({x}_{B_1},{y}_{B_1},{z}_{B_1}\right) \), \( {B}_2\left({x}_{B_2},{y}_{B_2},{z}_{B_2}\right) \), and \( {B}_3\left({x}_{B_3},{y}_{B_3},{z}_{B_3}\right) \).
Calculating the direction vector of the subtube
We obtain \( \overrightarrow{M_{31}{M}_3}=\left({x}_3-{x}_{31},{y}_3-{y}_{31},{z}_3-{z}_{31}\right)=\left({m}_1,{n}_1,{k}_1\right) \)_{.}
When we find the maximum value \( {d}_{B_i} \), the coordinates of the bifurcation subtube probe in hematoma can be obtained as \( \frac{B_1{B}_{1i}}{2} \), \( \frac{B_2{B}_{2j}}{2} \), and \( \frac{B_3{B}_{3k}}{2} \).
Results and discussion
Therefore, the midpoint coordinates of the center axis M_{1}M_{2} are M_{1/2}(35.970, 5.836, 28.010), and the distance from M_{1} to M_{2} is d_{12} = ∣ M_{1}M_{2} ∣ ≈ 54.0377. Figure 8 is optimal external central axis of the hematoma.
The route planning for the puncture operation is as follows:
According to the point cloud search arithmetic method for modified adaptive weighted particle swarm optimization, we obtain the coordinates as M_{3}(26.201.451, 18.457) and M_{4}(36.353, −7.797, 37.816), and the distance from M_{3} to M_{4} is d_{34} = 38.134751.
In this study, 3D Slicer software was used to reconstruct a three-dimensional model of an intracranial hematoma, and an improved algorithm based on clustering and adaptive particle swarm optimization to extract the optimal external central axis of the hematoma was proposed. According to the characteristics of the drainage tube and the specific morphology of the intracranial hematoma, optimal path planning under ideal conditions was performed, and the preoperative simulation was provided, which will be useful in clinical medicine.
The bifurcation localization algorithm of the drainage tube proposed in this study assumes an ideal state. It is necessary to consider the distribution of peripheral nerves and blood vessels and the specific location of the hematoma in the brain to apply this method to clinical medicine. The focus of subsequent research work will be to optimize the algorithm by considering the above factors, to implement the diffusion model and simulation of the drug injection, to plan the optimal surgical project, and to provide a dynamic simulation before operation.
Notes
Acknowledgments
Authors thank Tangshan Gongren Hospital for their support of original data.
Authors’ contributions
All authors read and approved the final manuscript. Conceptualization, JChang, JCui, QP; methodology, QP and WZ; software, WZ and XZ; formal analysis, QP; resources, JChang; data curation, JChang and JCui; writing—original draft preparation, QP; writing—review and editing, QP; visualization, QP and WZ; supervision, JChang and JCui; funding acquisition, JChang.
Funding
This research was funded by the National Science Foundation of China, Nos. 51674121 and 61702184; the Returned Overseas Scholar Funding of Hebei Province, No. C2015005014; the Hebei Key Laboratory of Science and Application, and Tangshan Innovation Team Project, No. 18130209B.
Competing interests
The authors declare that they have no competing interests.
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