CN115619943A - Three-dimensional reconstruction method and system for surgical images - Google Patents
Three-dimensional reconstruction method and system for surgical images Download PDFInfo
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Abstract
The invention discloses a three-dimensional reconstruction method and a three-dimensional reconstruction system for surgical images, wherein the method comprises the following steps: s1, acquiring a surgical two-dimensional image; s2, preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image; s3, segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting the characteristic information of the images to obtain a segmentation sequence corresponding to each tissue; s4, performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image; and S5, dynamically demonstrating the three-dimensional image. The invention can carry out three-dimensional reconstruction on the surgical two-dimensional image, and improves the efficiency of clinical diagnosis and treatment.
Description
Technical Field
The invention relates to the technical field of three-dimensional reconstruction, in particular to a surgical image three-dimensional reconstruction method and a surgical image three-dimensional reconstruction system.
Background
At present, a clinician acquires a two-dimensional tomographic image of a human tissue structure of a patient by using imaging equipment such as CT, MRI, ultrasound, and the like, observes and extracts related information, and then provides a reasonable diagnosis and treatment scheme for an illness state. However, since the shape size, the spatial relative position and the adjacent relationship of the human organ tissues in the two-dimensional images are fuzzy, it is difficult for doctors to obtain a clear three-dimensional stereoscopic impression, and only by observing a plurality of two-dimensional tomographic images and the clinical experience accumulated by themselves, the shape size of the focus of a patient and the relative position of the surrounding tissue organs and the like can be inferred, and a corresponding diagnosis can be made accordingly. Therefore, the conversion of two-dimensional images into three-dimensional images has become an urgent need for clinical diagnosis and treatment in the present day.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for three-dimensional reconstruction of a surgical image, which can be used for three-dimensional reconstruction of a surgical two-dimensional image and improve the efficiency of clinical diagnosis and treatment.
In a first aspect:
the invention provides a three-dimensional reconstruction method of a surgical image, which comprises the following steps:
s1, acquiring a surgical two-dimensional image;
s2, preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
s3, segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting characteristic information of the images, and obtaining a segmentation sequence corresponding to each tissue;
s4, performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and S5, dynamically demonstrating the three-dimensional image.
Preferably, the step S2 includes:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image.
Preferably, the step S3 includes:
s31, carrying out histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
s32, extracting the edge information of the image to be segmented by utilizing a Sobel algorithm;
s33, extracting the regional characteristics of the image to be segmented by utilizing U-Net;
s34, fusing the edge information and the regional characteristics by using a concat fusion mode to obtain a fusion result;
s35, performing multilayer convolution operation on the fusion result, and outputting a convolution result;
s36, performing Softmax on the convolution result to obtain a segmentation image; the segmented image comprises images of a plurality of tissues;
and S37, extracting the characteristic information of the segmentation image to obtain a segmentation sequence corresponding to each tissue.
Preferably, the step S4 includes:
s41, extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
s42, selecting the triangular patch corresponding to the extracted isosurface;
s43, smoothing the selected triangular patch;
and S44, connecting the isosurface of the smoothed surface patch, and reconstructing a three-dimensional image corresponding to the surgical two-dimensional image.
Preferably, the step S5 further includes:
and carrying out moving, rotating, amplifying and reducing operations on the three-dimensional image according to the instruction.
In a second aspect:
the invention provides a surgical image three-dimensional reconstruction system, comprising:
an acquisition module for acquiring a surgical two-dimensional image;
the preprocessing module is used for preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
the segmentation module is used for segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting the characteristic information of the images and obtaining a segmentation sequence corresponding to each tissue;
the reconstruction module is used for performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and the demonstration module is used for dynamically demonstrating the three-dimensional image.
Preferably, the preprocessing module is specifically configured to:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image.
Preferably, the segmentation module is specifically configured to:
performing histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
extracting edge information of the image to be segmented by using a Sobel algorithm;
extracting the regional characteristics of the image to be segmented by utilizing U-Net;
fusing the edge information and the region characteristics by using a concat fusion mode to obtain a fusion result;
carrying out multilayer convolution operation on the fusion result, and outputting a convolution result;
performing Softmax on the convolution result to obtain a segmentation image; the segmented image comprises images of a plurality of tissues;
and extracting the characteristic information of the segmented image to obtain a segmentation sequence corresponding to each tissue.
Preferably, the reconstruction module is specifically configured to:
extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
selecting the triangular patch corresponding to the extracted isosurface;
carrying out smoothing treatment on the selected triangular patch;
and connecting the equivalent surfaces of the smoothed surface patches to reconstruct a three-dimensional image corresponding to the surgical two-dimensional image.
Preferably, the demonstration module is further configured to perform operations of moving, rotating, zooming in and zooming out on the three-dimensional image according to an instruction.
The invention has the beneficial effects that:
the two-dimensional image can be denoised through processing such as dimension transformation, filtering, sharpening and the like, so that the accuracy of the three-dimensional image is improved; the histogram equalization processing is carried out on the two-dimensional image, so that the contrast of the two-dimensional image can be enhanced, and the gray level distribution of the two-dimensional image is more uniform; the Sobel algorithm is used for reserving image edge detail information, the edge information is used for assisting in segmentation, the detail information of the two-dimensional image can be reserved as far as possible, and the accuracy and precision of segmentation are effectively improved; the doctor can move, rotate, enlarge and reduce the three-dimensional image through a mouse or a touch screen, and the doctor can conveniently check the three-dimensional image.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a schematic flow chart of a method for three-dimensional reconstruction of a surgical image according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a surgical image three-dimensional reconstruction system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, an embodiment of the present invention provides a method for three-dimensional reconstruction of a surgical image, including the following steps:
s1, acquiring a surgical two-dimensional image;
s2, preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
s3, segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting characteristic information of the images, and obtaining a segmentation sequence corresponding to each tissue;
s4, performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and S5, dynamically demonstrating the three-dimensional image.
Wherein the two-dimensional image may be a two-dimensional CT image of the lung, liver, heart or kidney. In the liver, the tissues include liver, blood vessels, bones, and the like. According to the embodiment of the invention, the three-dimensional reconstruction can be carried out on the surgical two-dimensional image, so that the efficiency of clinical diagnosis and treatment is improved.
Further, step S2 includes:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image. Through the processing of dimension transformation, filtering, sharpening and the like, the two-dimensional image can be denoised, and the accuracy of the three-dimensional image is further improved.
Further, the air conditioner is provided with a fan,
the step S3 comprises the following steps:
s31, carrying out histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
s32, extracting edge information of the image to be segmented by utilizing a Sobel algorithm;
s33, extracting the regional characteristics of the image to be segmented by utilizing U-Net;
s34, fusing the edge information and the regional characteristics by using a concat fusion mode to obtain a fusion result;
s35, performing multilayer convolution operation on the fusion result, and outputting a convolution result;
s36, performing Softmax on the convolution result to obtain a segmentation image; the segmented image comprises images of a plurality of tissues;
and S37, extracting the characteristic information of the segmentation image to obtain a segmentation sequence corresponding to each tissue.
In the embodiment of the invention, the histogram equalization processing is firstly carried out on the two-dimensional image, so that the contrast of the two-dimensional image can be enhanced, and the gray level distribution of the two-dimensional image is more uniform. Then, the edge information and the regional characteristics are respectively extracted, so that the organization and clues in the organization can be effectively utilized. And the Sobel algorithm is used for reserving image edge detail information, the edge information is used for assisting in segmentation, the detail information of the two-dimensional image can be reserved as far as possible, and the accuracy and precision of segmentation are effectively improved.
Further, step S4 includes:
s41, extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
s42, selecting the triangular patch corresponding to the extracted isosurface;
s43, smoothing the selected triangular patch;
and S44, connecting the isosurface of the smoothed surface patch, and reconstructing a three-dimensional image corresponding to the surgical two-dimensional image.
In the embodiment of the invention, the extraction of the isosurface can be distributed in each voxel by the moving cube algorithm, the isosurface in each processed voxel is approximated by a triangular patch, and when the triangular patch after optimization processing is smoothed, the Laplace smoothing technology can be adopted, corresponding iteration times are set, and surface noise points are reduced by adjusting the positions of points.
Further, step S5 further includes: and carrying out moving, rotating, zooming-in and zooming-out operations on the three-dimensional image according to the instruction.
In the embodiment of the invention, a doctor can move, rotate, enlarge and reduce the three-dimensional image through a mouse or a touch screen, so that the doctor can conveniently check the three-dimensional image better.
Example two:
based on the same inventive concept as the first embodiment, an embodiment of the present invention provides a three-dimensional reconstruction system for surgical images, as shown in fig. 2, including:
an acquisition module for acquiring a surgical two-dimensional image;
the preprocessing module is used for preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
the segmentation module is used for segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting the characteristic information of the images and obtaining a segmentation sequence corresponding to each tissue;
the reconstruction module is used for performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and the demonstration module is used for dynamically demonstrating the three-dimensional image.
According to the embodiment of the invention, the three-dimensional reconstruction can be carried out on the surgical two-dimensional image, so that the efficiency of clinical diagnosis and treatment is improved.
The preprocessing module is specifically configured to:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image.
In the embodiment of the invention, the two-dimensional image can be denoised through processing such as dimension transformation, filtering, sharpening and the like, so that the accuracy of the three-dimensional image is improved.
The segmentation module is specifically configured to:
performing histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
extracting edge information of an image to be segmented by using a Sobel algorithm;
extracting the regional characteristics of the image to be segmented by utilizing U-Net;
fusing the edge information and the regional characteristics by using a concat fusion mode to obtain a fusion result;
performing multilayer convolution operation on the fusion result, and outputting a convolution result;
performing Softmax on the convolution result to obtain a segmentation image, wherein the segmentation image comprises images of a plurality of tissues;
and extracting the characteristic information of the segmented image to obtain a segmentation sequence corresponding to each tissue.
In the embodiment of the invention, the histogram equalization processing is firstly carried out on the two-dimensional image, so that the contrast of the two-dimensional image can be enhanced, and the gray level distribution of the two-dimensional image is more uniform. Then, the edge information and the regional characteristics are respectively extracted, so that clues of tissues (such as lung and liver) and inside of the tissues (such as lung and liver) can be effectively utilized. And the Sobel algorithm is used for reserving image edge detail information, the edge information is used for assisting segmentation, the detail information of the two-dimensional image can be reserved as far as possible, and the accuracy and precision of segmentation are effectively improved.
The reconstruction module is specifically configured to:
extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
selecting the triangular patch corresponding to the extracted isosurface;
carrying out smoothing treatment on the selected triangular patch;
and connecting the equivalent surfaces of the smoothed surface patches to reconstruct a three-dimensional image corresponding to the surgical two-dimensional image.
In the embodiment of the invention, the extraction of the isosurface can be distributed in each voxel by the moving cube algorithm, the isosurface in each processed voxel is approximated by a triangular patch, and when the triangular patch after optimization processing is smoothed, the Laplace smoothing technology can be adopted, corresponding iteration times are set, and surface noise points are reduced by adjusting the positions of points.
The demonstration module is also used for carrying out moving, rotating, amplifying and reducing operations on the three-dimensional image according to the instruction.
In the embodiment of the invention, a doctor can move, rotate, enlarge and reduce the three-dimensional image through a mouse or a touch screen, so that the doctor can conveniently view the three-dimensional image.
The embodiment of the invention provides a three-dimensional reconstruction method and a three-dimensional reconstruction system for a surgical image, wherein a two-dimensional image can be denoised through dimension transformation, filtering, sharpening and other processing, so that the accuracy of the three-dimensional image is improved; the histogram equalization processing is carried out on the two-dimensional image, so that the contrast of the two-dimensional image can be enhanced, and the gray level distribution of the two-dimensional image is more uniform; the Sobel algorithm is used for reserving image edge detail information, the edge information is used for assisting in segmentation, the detail information of the two-dimensional image can be reserved as far as possible, and the accuracy and precision of segmentation are effectively improved; the doctor can move, rotate, enlarge and reduce the three-dimensional image through the mouse or the touch screen, and the doctor can conveniently check the three-dimensional image better.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A method for three-dimensional reconstruction of surgical images, comprising the steps of:
s1, acquiring a surgical two-dimensional image;
s2, preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
s3, segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues; extracting the characteristic information of the image to obtain a segmentation sequence corresponding to each tissue;
s4, performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and S5, dynamically demonstrating the three-dimensional image.
2. A method for three-dimensional reconstruction of a surgical image according to claim 1, wherein said step S2 comprises:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image.
3. A method for three-dimensional reconstruction of a surgical image according to claim 1, wherein said step S3 comprises:
s31, carrying out histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
s32, extracting the edge information of the image to be segmented by using a Sobel algorithm;
s33, extracting the regional characteristics of the image to be segmented by utilizing U-Net;
s34, fusing the edge information and the regional characteristics by using a concat fusion mode to obtain a fusion result;
s35, performing multilayer convolution operation on the fusion result, and outputting a convolution result;
s36, performing Softmax on the convolution result to obtain a segmentation image; the segmented image comprises images of a plurality of tissues;
and S37, extracting the characteristic information of the segmentation image to obtain a segmentation sequence corresponding to each tissue.
4. A method for three-dimensional reconstruction of a surgical image according to claim 1, wherein said step S4 comprises:
s41, extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
s42, selecting the triangular patch corresponding to the extracted isosurface;
s43, smoothing the selected triangular patch;
and S44, connecting the isosurface of the smoothed surface patch, and reconstructing a three-dimensional image corresponding to the surgical two-dimensional image.
5. A method for three-dimensional reconstruction of a surgical image according to claim 1, wherein said step S5 further comprises:
and carrying out moving, rotating, amplifying and reducing operations on the three-dimensional image according to the instruction.
6. A system for three-dimensional reconstruction of surgical images, comprising:
an acquisition module for acquiring a surgical two-dimensional image;
the preprocessing module is used for preprocessing the two-dimensional image to obtain a preprocessed two-dimensional image;
the segmentation module is used for segmenting the preprocessed two-dimensional image to obtain images of a plurality of tissues, extracting the characteristic information of the images and obtaining a segmentation sequence corresponding to each tissue;
the reconstruction module is used for performing three-dimensional reconstruction on each tissue based on a preset three-dimensional reconstruction method according to the segmentation sequence corresponding to each tissue to obtain a three-dimensional image;
and the demonstration module is used for dynamically demonstrating the three-dimensional image.
7. A system for three-dimensional reconstruction of surgical images according to claim 6, characterized in that said preprocessing module is specifically configured to:
and carrying out dimension transformation, filtering and sharpening on the two-dimensional image to obtain a preprocessed two-dimensional image.
8. A system for three-dimensional reconstruction of surgical images according to claim 6, wherein said segmentation module is specifically configured to:
performing histogram equalization processing on the preprocessed two-dimensional image to obtain an image to be segmented;
extracting edge information of the image to be segmented by using a Sobel algorithm;
extracting the regional characteristics of the image to be segmented by utilizing U-Net;
fusing the edge information and the region characteristics by using a concat fusion mode to obtain a fusion result;
performing multilayer convolution operation on the fusion result, and outputting a convolution result;
performing Softmax on the convolution result to obtain a segmentation image; the segmented image comprises images of a plurality of tissues;
and extracting the characteristic information of the segmented image to obtain a segmentation sequence corresponding to each tissue.
9. A system for three-dimensional reconstruction of surgical images according to claim 6, wherein said reconstruction module is specifically configured to:
extracting an isosurface from the segmentation sequence by adopting a mobile cube algorithm;
selecting the triangular patch corresponding to the extracted isosurface;
carrying out smoothing treatment on the selected triangular patch;
and connecting the equivalent surfaces of the smoothed surface patches to reconstruct a three-dimensional image corresponding to the surgical two-dimensional image.
10. A surgical image three-dimensional reconstruction system as claimed in claim 6, wherein said demonstration module is further configured to perform operations of moving, rotating, zooming in and zooming out on said three-dimensional image according to instructions.
Priority Applications (1)
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