CN108670409B - Three-dimensional lung tissue reconstruction and visualization device for surgical planning - Google Patents
Three-dimensional lung tissue reconstruction and visualization device for surgical planning Download PDFInfo
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Abstract
The invention relates to a three-dimensional reconstruction and visualization device of lung tissue for surgical planning, comprising: a computer programmed to perform the steps of: the method comprises the following steps: receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by integers with values of 0-9 on the basis of read-in image space coordinates and respectively represent different tissues; step two: performing median filtering on all layers of the CT image; step three: in the CT image, a spatial region is selected for a region to be observed. The lung tissue three-dimensional reconstruction and visualization device for surgical planning is specially used for three-dimensional reconstruction and visualization of pulmonary nodule surgery, the system is stable and reliable in work, and the clinical applicability is strong; the simple and efficient segmentation is adopted for the segmentation of the trachea, the blood vessel and the nodule, the working process is simple, the operation is simple and convenient, and the efficiency is high.
Description
Technical Field
The invention relates to lung tissue, in particular to a three-dimensional reconstruction and visualization device for lung tissue for surgical planning.
Background
Because the incidence of lung tumors is high, the lung tumors are the most common form of multiple tumors at present, and the surgical removal of tumor sites is a main means. Three-dimensional reconstruction and visualization of pulmonary nodules and pulmonary tissue is an important method for preoperative planning. At present, three-dimensional reconstruction and visualization methods for surgical planning of pulmonary nodules are mainly based on developed segmentation algorithms based on CT images, and clinical verification and development are few.
The prior art is mainly focused on the segmentation and reconstruction of the trachea aiming at the three-dimensional reconstruction and visualization of pulmonary nodules and tissues. Segmentation and visualization algorithms and software for the lung's entire tissue and nodules are mainly achieved by integrating existing segmentation methods. The existing software comprises Mimics and OsiriX, and a regional growth method is adopted to segment the trachea. Slicer, the user needs to independently select and combine segmentation methods to segment different tissues and nodules of the lung. And DeepInSight, which adopts a region growing method to segment and reconstruct each tissue in three dimensions.
The references are as follows:
Reynisson,Pall Jens,Marta Scali,Erik Smistad,Erlend Fagertun Hofstad,
Olav Leira,Frank Lindseth,Toril Anita Nagelhus Hernes,Tore Amundsen,Hanne Sorger,and Thomas
.2015.“Airway Segmentation and CenterlineExtraction from Thoracic CT-Comparison of a New Method to State of the ArtCommercialized Methods.”PLoS ONE 10(12):1–20.doi:10.1371/journal.pone.0144282.
the traditional technology has the following technical problems:
universal software such as mimics and the like has various segmentation algorithms which can be selected and used, but no special segmentation is carried out on lung tissues, organs and nodules, the segmentation and reconstruction operations are complex, and the clinical application is difficult; the existing multiple segmentation methods have large calculation amount and low efficiency, and are difficult to meet the requirements of rapid segmentation reconstruction and visualization of lesions and lung organ tissues before pulmonary nodule operation; the existing special software has low stability and complex operation, and can not realize the reconstruction and visualization of local areas.
Disclosure of Invention
Based on this, it is necessary to provide a three-dimensional reconstruction and visualization device for pulmonary tissue for surgical planning, which is specially directed to the three-dimensional reconstruction and visualization of pulmonary nodule surgery, and has stable and reliable system operation and strong clinical applicability; the trachea, the blood vessel and the nodule are segmented simply and efficiently, the working process is simple, the operation is simple and convenient, and the efficiency is high; the local region may be segmented and visualized.
A three-dimensional reconstruction and visualization apparatus of lung tissue for surgical planning, comprising: a computer programmed to perform the steps of:
the method comprises the following steps: receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by integers with values of 0-9 on the basis of read-in image space coordinates and respectively represent different tissues;
step two: performing median filtering on all layers of the CT image;
step three: selecting a space region aiming at a part needing to be observed in the CT image;
step four: selecting a typical region in a target region to be segmented and reconstructed within a selectable threshold range of-1024 pixel values to 1023 pixel values, analyzing pixel distribution, and calculating a mean value and a variance; setting a segmentation threshold value based on the mean value of the pixel distribution, and generating a label map corresponding to the target area; when the variance is larger than 50, adjusting specific pixel values in the neighborhood of positive and negative 50 pixel values of the mean value, and observing and obtaining a region segmentation result; otherwise, adjusting the specific pixel value by the actual pixel value, observing and obtaining a region segmentation result; executing the operation on all layers of the CT image;
step five: selecting one or a plurality of mark point sets Lambda 0 as initial values in the divided area, selecting a communication area in a three-dimensional space based on the mark points, searching by adopting a 6, 18 or 26 communication area, outputting as a processed mark map, searching marked pixel points around the pixel based on the 6, 18 or 26 communication area in the searching process, and merging the found mark points with the initial mark point sets Lambda 0 to obtain a new pixel point set Lambda, (i is 1, …, n) wherein i is iterative computation step number; and repeating the search until all the mark points are found out to obtain a pixel set lambdan of the segmentation result.
Step six: and (5) performing three-dimensional reconstruction on the segmentation result lambdan, and if the segmentation result lambdan does not meet the requirement, repeating the third step, the fourth step and the fifth step to perform automatic segmentation again.
In another embodiment, where 0 is background, 1 is trachea, 2 is vein, 3 is artery, and 4-10 is tumor.
In another embodiment, the calculated radius of the median filter is 1 pixel to 3 pixels.
In another embodiment, the step "in the CT image, selecting a spatial region for a desired observation region; the method specifically comprises the following steps: based on a plane P1 of the CT image, two planes P2 and P3 perpendicular to the plane P1 are constructed, and a space region omega is selected in three orthogonal planes P1-P3.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
the method comprises the following steps: receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by integers with values of 0-9 on the basis of read-in image space coordinates and respectively represent different tissues;
step two: performing median filtering on all layers of the CT image;
step three: selecting a space region aiming at a part needing to be observed in the CT image;
step four: selecting a typical region in a target region to be segmented and reconstructed within a selectable threshold range of-1024 pixel values to 1023 pixel values, analyzing pixel distribution, and calculating a mean value and a variance; setting a segmentation threshold value based on the mean value of the pixel distribution, and generating a label map corresponding to the target area; when the variance is larger than 50, adjusting specific pixel values in the neighborhood of positive and negative 50 pixel values of the mean value, and observing and obtaining a region segmentation result; otherwise, adjusting the specific pixel value by the actual pixel value, observing and obtaining a region segmentation result; executing the operation on all layers of the CT image;
step five: selecting one or a plurality of mark point sets Lambda 0 as initial values in the divided area, selecting a communication area in a three-dimensional space based on the mark points, searching by adopting a 6, 18 or 26 communication area, outputting as a processed mark map, searching marked pixel points around the pixel based on the 6, 18 or 26 communication area in the searching process, and merging the found mark points with the initial mark point sets Lambda 0 to obtain a new pixel point set Lambda, (i is 1, …, n) wherein i is iterative computation step number; and repeating the search until all the mark points are found out to obtain a pixel set lambdan of the segmentation result.
Step six: and (5) performing three-dimensional reconstruction on the segmentation result lambdan, and if the segmentation result lambdan does not meet the requirement, repeating the third step, the fourth step and the fifth step to perform automatic segmentation again.
In another embodiment, where 0 is background, 1 is trachea, 2 is vein, 3 is artery, and 4-10 is tumor.
In another embodiment, the calculated radius of the median filter is 1 pixel to 3 pixels.
In another embodiment, the step "in the CT image, selecting a spatial region for a desired observation region; the method specifically comprises the following steps: based on a plane P1 of the CT image, two planes P2 and P3 perpendicular to the plane P1 are constructed, and a space region omega is selected in three orthogonal planes P1-P3.
The lung tissue three-dimensional reconstruction and visualization device for surgical planning is specially used for three-dimensional reconstruction and visualization of pulmonary nodule surgery, the system is stable and reliable in work, and the clinical applicability is strong; the trachea, the blood vessel and the nodule are segmented simply and efficiently, the working process is simple, the operation is simple and convenient, and the efficiency is high; the local region may be segmented and visualized.
Drawings
Fig. 1 is a flowchart executed by a computer in a three-dimensional reconstruction and visualization apparatus for pulmonary tissue planning according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a three-dimensional reconstruction and visualization apparatus for pulmonary tissue planning, which is provided in an embodiment of the present application, for searching a communication region (6, 18, or 26 communication region).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A three-dimensional reconstruction and visualization apparatus of lung tissue for surgical planning, comprising: a computer programmed to perform the steps of:
the method comprises the following steps: receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by integers with values of 0-9 on the basis of read-in image space coordinates and respectively represent different tissues;
step two: performing median filtering on all layers of the CT image;
step three: selecting a space region aiming at a part needing to be observed in the CT image;
step four: selecting a typical region in a target region to be segmented and reconstructed within a selectable threshold range of-1024 pixel values to 1023 pixel values, analyzing pixel distribution, and calculating a mean value and a variance; setting a segmentation threshold value based on the mean value of the pixel distribution, and generating a label map corresponding to the target area; when the variance is larger than 50, adjusting specific pixel values in the neighborhood of positive and negative 50 pixel values of the mean value, and observing and obtaining a region segmentation result; otherwise, adjusting the specific pixel value by the actual pixel value, observing and obtaining a region segmentation result; executing the operation on all layers of the CT image;
step five: selecting one or a plurality of mark point sets Lambda 0 as initial values in the divided area, selecting a communication area in a three-dimensional space based on the mark points, searching by adopting a 6, 18 or 26 communication area, outputting as a processed mark map, searching marked pixel points around the pixel based on the 6, 18 or 26 communication area in the searching process, and merging the found mark points with the initial mark point sets Lambda 0 to obtain a new pixel point set Lambda, (i is 1, …, n) wherein i is iterative computation step number; and repeating the search until all the mark points are found out to obtain a pixel set lambdan of the segmentation result.
Step six: and (5) performing three-dimensional reconstruction on the segmentation result lambdan, and if the segmentation result lambdan does not meet the requirement, repeating the third step, the fourth step and the fifth step to perform automatic segmentation again.
In another embodiment, where 0 is background, 1 is trachea, 2 is vein, 3 is artery, and 4-10 is tumor.
In another embodiment, the calculated radius of the median filter is 1 pixel to 3 pixels.
In another embodiment, the step "in the CT image, selecting a spatial region for a desired observation region; the method specifically comprises the following steps: based on a plane P1 of the CT image, two planes P2 and P3 perpendicular to the plane P1 are constructed, and a space region omega is selected in three orthogonal planes P1-P3. The spatial region Ω is a range region of the subsequent calculation processing.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
the method comprises the following steps: receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by integers with values of 0-9 on the basis of read-in image space coordinates and respectively represent different tissues;
step two: performing median filtering on all layers of the CT image;
step three: selecting a space region aiming at a part needing to be observed in the CT image;
step four: selecting a typical region in a target region to be segmented and reconstructed within a selectable threshold range of-1024 pixel values to 1023 pixel values, analyzing pixel distribution, and calculating a mean value and a variance; setting a segmentation threshold value based on the mean value of the pixel distribution, and generating a label map corresponding to the target area; when the variance is larger than 50, adjusting specific pixel values in the neighborhood of positive and negative 50 pixel values of the mean value, and observing and obtaining a region segmentation result; otherwise, adjusting the specific pixel value by the actual pixel value, observing and obtaining a region segmentation result; executing the operation on all layers of the CT image;
step five: selecting one or a plurality of mark point sets Lambda 0 as initial values in the divided area, selecting a communication area in a three-dimensional space based on the mark points, searching by adopting a 6, 18 or 26 communication area, outputting as a processed mark map, searching marked pixel points around the pixel based on the 6, 18 or 26 communication area in the searching process, and merging the found mark points with the initial mark point sets Lambda 0 to obtain a new pixel point set Lambda, (i is 1, …, n) wherein i is iterative computation step number; and repeating the search until all the mark points are found out to obtain a pixel set lambdan of the segmentation result.
Step six: and (5) performing three-dimensional reconstruction on the segmentation result lambdan, and if the segmentation result lambdan does not meet the requirement, repeating the third step, the fourth step and the fifth step to perform automatic segmentation again.
The combined use of the above steps realizes the segmentation reconstruction of trachea, artery, vein vessel and rib.
In another embodiment, where 0 is background, 1 is trachea, 2 is vein, 3 is artery, and 4-10 is tumor.
In another embodiment, the calculated radius of the median filter is 1 pixel to 3 pixels.
In another embodiment, the step "in the CT image, selecting a spatial region for a desired observation region; the method specifically comprises the following steps: based on a plane P1 of the CT image, two planes P2 and P3 perpendicular to the plane P1 are constructed, and a space region omega is selected in three orthogonal planes P1-P3.
The lung tissue three-dimensional reconstruction and visualization device for surgical planning is specially used for three-dimensional reconstruction and visualization of pulmonary nodule surgery, the system is stable and reliable in work, and the clinical applicability is strong; the trachea, the blood vessel and the nodule are segmented simply and efficiently, the working process is simple, the operation is simple and convenient, and the efficiency is high; the local region may be segmented and visualized.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (1)
1. A three-dimensional reconstruction and visualization apparatus for pulmonary tissue for surgical planning, comprising: a computer programmed to perform the steps of:
receiving an imported Dicom-format CT image, and respectively establishing corresponding label maps aiming at target tissues needing to be segmented and three-dimensionally reconstructed, wherein the label maps are indexed by an integer with the value of 0 ~ 9 based on read-in image space coordinates and respectively represent different tissues;
step two: performing median filtering on all layers of the CT image;
step three: selecting a space region aiming at a part needing to be observed in the CT image;
selecting a typical region in a target region to be segmented and reconstructed within a selectable threshold range of a-1024 pixel value ~ 1023 pixel value, analyzing pixel distribution, calculating a mean value and a variance, setting a segmentation threshold value based on the mean value of the pixel distribution, and generating a labeled graph corresponding to the target region, wherein when the variance is more than 50, the segmentation threshold value is adjusted in the neighborhood of plus or minus 50 pixel values of the mean value, and a region segmentation result is observed and obtained;
step five: in the divided area, a plurality of mark point sets Λ 0 serve as initial values, linking areas in a three-dimensional space are selected based on the mark points, 6, 18 or 26 linking areas are adopted for searching, a processed mark diagram is output, in the searching process, marked pixel points on the periphery of pixels are searched based on the 6, 18 or 26 linking areas, the found mark points and the initial mark point sets Λ 0 are combined, and a new pixel point set Λ i is obtained, wherein i =1, …, n is obtained, and i is the number of iterative calculation steps; repeatedly searching until all the mark points are found out to obtain a pixel point set lambdan of the segmentation result;
step six: performing three-dimensional reconstruction on the pixel point set lambdan of the segmentation result, and if the pixel point set lambdan does not meet the requirement, repeating the third step, the fourth step and the fifth step for automatic segmentation;
the calculation radius value of the median filtering is 1 pixel to 3 pixels;
the step of "selecting a spatial region for a region to be observed in the CT image" specifically includes: based on a plane P1 of the CT image, two planes P2 and P3 perpendicular to the plane P1 are constructed, and a space region omega is selected in three orthogonal planes P1-P3.
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