CN115409819A - Liver image reconstruction method and reconstruction system - Google Patents
Liver image reconstruction method and reconstruction system Download PDFInfo
- Publication number
- CN115409819A CN115409819A CN202211079340.9A CN202211079340A CN115409819A CN 115409819 A CN115409819 A CN 115409819A CN 202211079340 A CN202211079340 A CN 202211079340A CN 115409819 A CN115409819 A CN 115409819A
- Authority
- CN
- China
- Prior art keywords
- image
- liver
- feature map
- reconstruction
- map
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 210000004185 liver Anatomy 0.000 title claims abstract description 134
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000000007 visual effect Effects 0.000 claims abstract description 55
- 210000004204 blood vessel Anatomy 0.000 claims abstract description 25
- 206010019695 Hepatic neoplasm Diseases 0.000 claims abstract description 24
- 230000004927 fusion Effects 0.000 claims abstract description 24
- 208000014018 liver neoplasm Diseases 0.000 claims abstract description 24
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 20
- 230000002440 hepatic effect Effects 0.000 claims abstract description 17
- 238000010586 diagram Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims description 15
- 230000000877 morphologic effect Effects 0.000 claims description 14
- 238000000605 extraction Methods 0.000 claims description 8
- 230000003628 erosive effect Effects 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 7
- 230000010339 dilation Effects 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 2
- 206010028980 Neoplasm Diseases 0.000 abstract description 10
- 238000001356 surgical procedure Methods 0.000 description 6
- 239000010410 layer Substances 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000002679 ablation Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 230000010102 embolization Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 239000011229 interlayer Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000002054 transplantation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
- G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30056—Liver; Hepatic
Abstract
The invention provides a liver image reconstruction method and a reconstruction system, wherein the method comprises the following steps: acquiring a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient; respectively extracting a global semantic feature map and a local visual feature map based on the CT image; performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map; and performing three-dimensional image reconstruction based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image. The invention can automatically and accurately identify the relative position of the hepatic vessels and the hepatic tumors, and is convenient for doctors to make an operation scheme according to the relative position of the hepatic vessels and the hepatic tumors of each patient.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a liver image reconstruction method and a liver image reconstruction system.
Background
At present, computer-assisted liver surgery (such as ablation and embolization) is increasingly used for treating patients with primary and secondary liver tumors which do not meet the surgical conditions or as a bridge for liver transplantation, and doctors can segment liver blood vessels, tumors and the like according to CT images before surgery so as to assist the liver interventional surgery in three-dimensional visualization, path planning and guidance, but some problems still exist in computer-assisted liver interventional surgery, and the most important point is that the segmentation of liver blood vessels and tumors is usually manually made on CT images by doctors, so that omission occurs inevitably, and time and labor are consumed, and thus liver blood vessels which are not accurately positioned for supplying blood to tumors are caused, so that liver embolization surgery is influenced, and information such as relative positions of liver blood vessels and liver tumors and diameters of blood vessels cannot be accurately identified, so that ablation surgery is influenced, and local tumors recur.
Disclosure of Invention
The invention aims to provide a liver image reconstruction method and a liver image reconstruction system, which can automatically and accurately identify the relative position of hepatic vessels and liver tumors and facilitate doctors to make an operation scheme according to the relative position of the hepatic vessels and the liver tumors of each patient.
In a first aspect, the present invention provides a liver image reconstruction method, including:
acquiring a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient;
respectively extracting a global semantic feature map and a local visual feature map based on the CT image;
performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map;
and performing three-dimensional image reconstruction based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image.
Optionally, the respectively extracting a global semantic feature map and a local visual feature map based on the CT image includes:
carrying out feature extraction and dimension reduction on the CT image to obtain a global semantic feature map;
cutting the CT image according to a preset standard to obtain a local characteristic image;
and extracting visual features in the local feature map to serve as a local visual feature map.
Optionally, the performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map, includes:
fusing and recovering the global semantic feature map and the local visual feature map in a low dimension by using a residual error network to match with sampling;
and performing channel dimension fusion on the global semantic feature map and the local visual feature map with the same scale to obtain a liver feature map.
Optionally, the reconstructing a three-dimensional image based on the liver feature map and outputting a reconstruction result includes:
carrying out gray level processing on the liver characteristic map to determine a first liver characteristic map;
constructing a three-dimensional data field based on the first liver feature map, and selecting data with the highest gray value as an initial liver image;
processing the initial liver image by using morphological operation to determine a target liver image, wherein the morphological operation comprises at least one of dilation, erosion, opening operation and closing operation;
and performing three-dimensional image reconstruction based on the target liver image, and outputting a reconstruction result.
Optionally, the method further includes:
and storing the reconstruction result to a user database, wherein the file format of the reconstruction result in the user database is nrrd format.
In a second aspect, the present invention provides a liver image reconstruction system, including:
the system comprises an image acquisition module, a display module and a display module, wherein the image acquisition module is used for acquiring a CT image of a patient in a multi-scale receptive field, and the CT image is a liver tumor image or a liver blood vessel image of the patient;
the characteristic map determining module is used for respectively extracting a global semantic characteristic map and a local visual characteristic map based on the CT image;
the feature fusion module is used for performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map;
and the three-dimensional reconstruction module is used for reconstructing a three-dimensional image based on the liver characteristic diagram and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image.
Optionally, the feature map determining module includes:
the global feature acquisition unit is used for carrying out feature extraction and dimension reduction on the CT image to obtain a global semantic feature map;
the local feature acquisition unit is used for cutting the CT image according to a preset standard to obtain a local feature map; and extracting the visual features in the local feature map to be used as a local visual feature map.
Optionally, the feature fusion module includes:
the recovery unit is used for fusing and recovering the low-dimensional global semantic feature map and the local visual feature map by using residual error network matched sampling;
and the fusion unit is used for fusing the global semantic feature map and the local visual feature map with the same scale as a channel dimension to obtain a liver feature map.
Optionally, the three-dimensional reconstruction module includes:
the gray processing unit is used for carrying out gray processing on the liver characteristic map and determining a first liver characteristic map;
the image screening unit is used for constructing a three-dimensional data field based on the first liver characteristic diagram and initiating a liver image;
an operation unit for processing the initial liver image by using morphological operation to determine a target liver image, wherein the morphological operation comprises at least one of dilation, erosion, opening operation and closing operation;
and the three-dimensional image reconstruction unit is used for reconstructing a three-dimensional image based on the target liver image and outputting a reconstruction result.
Optionally, the method further includes:
and the user database is used for storing the reconstruction result, and the file format of the reconstruction result in the user database is the nrrd format.
The embodiment of the invention provides a liver image reconstruction method and a reconstruction system, which can acquire a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient; respectively extracting a global semantic feature map and a local visual feature map based on the CT image; performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map; and reconstructing a three-dimensional image based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional hepatic blood vessel image. The invention uses the deep learning technology (convolution neural network) to complete the fusion of the global and local characteristics of the CT image of the patient, and the fused liver characteristic diagram contains more accurate liver tumor or liver blood vessel information of the patient.
In addition, because the convolutional neural network is adopted in the scheme, the information of hepatic vessels, hepatic tumors and the like can be automatically identified after the convolutional neural network is trained, hands are liberated, the condition that a doctor manually adds the hepatic vessels, the hepatic tumors and the like is avoided, the whole process is quickly and automatically completed, the intervention of professionals is not needed, and meanwhile, the system is high in transportability, does not need to be specially installed, and is not limited by an operating system, computer configuration and regions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an alternative schematic diagram of a liver image reconstruction method in an embodiment of the invention;
FIG. 2 is an alternative diagram of the output of the reconstruction result of the three-dimensional image reconstruction in the embodiment of the present invention;
fig. 3 is a schematic structural diagram of a liver image reconstruction system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, the present invention provides a liver image reconstruction method, which specifically includes:
s11, acquiring a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient;
the receptive field is one of the important concepts of the convolutional neural network, and the multi-scale receptive field refers to the different ranges of the receptive field. If the receptive field is too small, the convolutional neural network can only observe the local features of the image; if the receptive field is too large, although the global information is more well understood, it usually contains many invalid information. Therefore, in the embodiment of the invention, the CT image under the multi-scale receptive field is obtained to provide sufficient image basis for the subsequent global semantic feature map and the local visual feature map.
Optionally, the CT image is a CT DICOM format picture.
And S12, respectively extracting a global semantic feature map and a local visual feature map based on the CT image.
And S13, performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map.
And S14, reconstructing a three-dimensional image based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image.
The invention relates to a liver image reconstruction method and a reconstruction system, which are used for acquiring a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient; respectively extracting a global semantic feature map and a local visual feature map based on the CT image; performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map; and reconstructing a three-dimensional image based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional hepatic blood vessel image. The invention uses the deep learning technology (convolution neural network) to complete the fusion of the global and local characteristics of the CT image of the patient, and the fused liver characteristic diagram contains more accurate liver tumor or liver blood vessel information of the patient.
In addition, because the convolutional neural network is adopted in the scheme, the information of hepatic vessels, hepatic tumors and the like can be automatically identified after the convolutional neural network is trained, hands are liberated, the condition that a doctor manually adds the hepatic vessels, the hepatic tumors and the like is avoided, the whole process is quickly and automatically completed, the intervention of professionals is not needed, and meanwhile, the system is high in transportability, does not need to be specially installed, and is not limited by an operating system, computer configuration and regions.
The following describes a specific implementation manner in the embodiment of the present invention.
In an embodiment, the extracting the global semantic feature map and the local visual feature map based on the CT image respectively may specifically include:
and performing feature extraction and dimension reduction on the CT image to obtain a global semantic feature map.
Optionally, a convolutional neural network multi-layer multi-head self-attention mechanism may be used to learn the CT image, and then the convolutional neural network is used to perform feature extraction and dimension reduction on the semantic related feature map, and the scales obtained are: 128 × 128, 64 × 64, 32 × 32, 16 × 16, etc. for different scales.
Further, the obtaining of the local visual feature map based on the CT image may be:
cutting the CT image according to a preset standard to obtain a local characteristic map;
and extracting the visual features in the local feature map to be used as a local visual feature map.
In the embodiment, the image in the CT DICOM format based on the same patient is cut according to the preset standard, and the visual features in the local feature map are extracted as the local visual feature map. Optionally, the local visual feature map may also be a feature map under multiple different scale receptive fields, 128 × 128, 64 × 64, 32 × 32, 16 × 16.
In a further embodiment of the present invention, the liver feature map may also be determined based on the global semantic feature map and the local visual feature map.
The specific steps for determining the liver characteristic map comprise:
fusing and recovering the global semantic feature map and the local visual feature map in a low dimension by using a residual error network to match with sampling; and fusing the global semantic feature map and the local visual feature map with the same scale as a channel dimension to obtain a liver feature map.
The residual error network is one of the convolutional neural networks, and specifically comprises the following steps: and (3) skipping the connection of neurons in the next layer by certain layers of the neural network, and connecting interlayer layers to weaken the strong connection between each layer.
Optionally, in the embodiment of the present invention, a residual error network is used to cooperate with sampling to fuse and recover the low-dimensional global semantic feature map and the local visual feature map; and fusing the global semantic feature map and the local visual feature map with the same scale as a channel dimension, and ensuring that a module effectively learns the features with different scales to obtain the liver feature map.
Further, after obtaining the liver feature map, referring to fig. 2, the three-dimensional image reconstruction process may be:
step S21, carrying out gray level processing on the liver characteristic map, and determining a first liver characteristic map;
in addition, after the liver characteristic map is obtained and before the gray level processing, the Gaussian filtering setting threshold value can be used for filtering out irrelevant noise in the liver characteristic map.
S22, constructing a three-dimensional data field based on the first liver feature map, and determining an initial liver image;
optionally, after the three-dimensional data field, data with the highest gray value is selected as an initial liver image.
And step S23, processing the initial liver image by using morphological operation to determine a target liver image.
The basic operations in the morphological operations include dilation, erosion, open operations, close operations, skeleton extraction, limit erosion, hit-on-miss transformation, morphological gradients, top-hat transformation, particle analysis, watershed transformation, gray value morphological gradients, and the like.
And S24, reconstructing a three-dimensional image based on the target liver image and outputting a reconstruction result.
Optionally, the reconstruction result may be obtained after reconstruction using a Visualization Tool Kit (VTK), and output in a form of a window.
In an optional implementation manner, the reconstruction result may be further stored in a user database, and a file format of the reconstruction result in the user database is an nrrd format.
In a further embodiment of the invention, a liver image reconstruction system is also provided. Referring to fig. 3, the liver image reconstruction system specifically includes:
the image acquisition module 300 is configured to acquire a CT image of a patient in a multi-scale receptive field, where the CT image is a liver tumor image or a liver blood vessel image of the patient;
a feature map determination module 400, configured to extract a global semantic feature map and a local visual feature map based on the CT image;
the feature fusion module 500 is configured to perform feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map;
and the three-dimensional reconstruction module 600 is configured to perform three-dimensional image reconstruction based on the liver feature map, and output a reconstruction result, where the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image.
Optionally, the feature map determining module 400 includes:
a global feature obtaining unit 410, configured to perform feature extraction and dimension reduction on the CT image to obtain a global semantic feature map;
the local feature obtaining unit 420 is configured to cut the CT image according to a preset standard to obtain a local feature map; and extracting visual features in the local feature map to serve as a local visual feature map.
Optionally, the feature fusion module 500 includes:
a recovering unit 510, configured to fuse and recover the low-dimensional global semantic feature map and the local visual feature map by using a residual network to match with sampling;
and a fusion unit 520, configured to fuse the global semantic feature map and the local visual feature map with the same scale by using channel dimensions to obtain a liver feature map.
Optionally, the three-dimensional reconstruction module 600 includes:
a gray processing unit 610, configured to perform gray processing on the liver feature map to determine a first liver feature map;
an image screening unit 620, configured to construct a three-dimensional data field based on the first liver feature map, and select data with a highest gray value as an initial liver image;
an operation unit 630, configured to process the initial liver image by using a morphological operation, wherein the morphological operation includes at least one of dilation, erosion, opening operation, and closing operation, to determine a target liver image;
and a three-dimensional image reconstruction unit 640, configured to perform three-dimensional image reconstruction based on the target liver image, and output a reconstruction result.
Optionally, the liver image reconstruction system further includes: and the user database 700 is used for storing the reconstruction result, and the file format of the reconstruction result in the user database is nrrd format.
The description of the liver image reconstruction method in this embodiment may refer to the description of the liver image reconstruction method, and will not be repeated here.
While various embodiments of the present invention have been described above, various alternatives described in the various embodiments can be combined and cross-referenced without conflict to extend the variety of possible embodiments that can be considered disclosed and disclosed in connection with the embodiments of the present invention.
Although the embodiments of the present invention have been disclosed, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A liver image reconstruction method, comprising:
acquiring a CT image of a patient in a multi-scale receptive field, wherein the CT image is a liver tumor image or a liver blood vessel image of the patient;
respectively extracting a global semantic feature map and a local visual feature map based on the CT image;
performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map;
and reconstructing a three-dimensional image based on the liver characteristic diagram, and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional hepatic blood vessel image.
2. The liver image reconstruction method of claim 1, wherein the extracting a global semantic feature map and a local visual feature map based on the CT image respectively comprises:
carrying out feature extraction and dimension reduction on the CT image to obtain a global semantic feature map;
cutting the CT image according to a preset standard to obtain a local characteristic graph;
and extracting visual features in the local feature map to serve as a local visual feature map.
3. The liver image reconstruction method according to claim 2, wherein the performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain the liver feature map comprises:
fusing and recovering the global semantic feature map and the local visual feature map in a low dimension by using a residual network to match with sampling;
and performing channel dimension fusion on the global semantic feature map and the local visual feature map with the same scale to obtain a liver feature map.
4. The liver image reconstruction method according to claim 3, wherein the performing three-dimensional image reconstruction based on the liver feature map and outputting a reconstruction result comprises:
carrying out gray level processing on the liver characteristic map to determine a first liver characteristic map;
constructing a three-dimensional data field based on the first liver feature map, and determining an initial liver image;
processing the initial liver image by using morphological operation to determine a target liver image, wherein the morphological operation comprises at least one of dilation, erosion, opening operation and closing operation;
and performing three-dimensional image reconstruction based on the target liver image, and outputting a reconstruction result.
5. The liver image reconstruction method of claim 4, further comprising:
and storing the reconstruction result to a user database, wherein the file format of the reconstruction result in the user database is nrrd format.
6. A liver image reconstruction system, comprising:
the system comprises an image acquisition module, a display module and a display module, wherein the image acquisition module is used for acquiring a CT image of a patient in a multi-scale receptive field, and the CT image is a liver tumor image or a liver blood vessel image of the patient;
the characteristic map determining module is used for respectively extracting a global semantic characteristic map and a local visual characteristic map based on the CT image;
the feature fusion module is used for performing feature fusion on the global semantic feature map and the local visual map by using a convolutional neural network to obtain a liver feature map;
and the three-dimensional reconstruction module is used for reconstructing a three-dimensional image based on the liver characteristic diagram and outputting a reconstruction result, wherein the reconstruction result is a three-dimensional liver tumor image or a three-dimensional liver blood vessel image.
7. The liver image reconstruction system of claim 6, wherein the feature map determination module comprises:
the global feature acquisition unit is used for carrying out feature extraction and dimension reduction on the CT image to obtain a global semantic feature map;
the local feature acquisition unit is used for cutting the CT image according to a preset standard to obtain a local feature map; and extracting visual features in the local feature map to serve as a local visual feature map.
8. The liver image reconstruction system of claim 7, wherein the feature fusion module comprises:
the recovery unit is used for fusing and recovering the low-dimensional global semantic feature map and the local visual feature map by using residual error network matched sampling;
and the fusion unit is used for fusing the global semantic feature map and the local visual feature map with the same scale by channel dimension to obtain a liver feature map.
9. The liver image reconstruction system of claim 7, wherein the three-dimensional reconstruction module comprises:
the gray level processing unit is used for carrying out gray level processing on the liver characteristic map and determining a first liver characteristic map;
the image screening unit is used for constructing a three-dimensional data field based on the first liver characteristic graph and selecting data with the highest gray value as an initial liver image;
an operation unit for processing the initial liver image by using morphological operation to determine a target liver image, wherein the morphological operation comprises at least one of dilation, erosion, opening operation and closing operation;
and the three-dimensional image reconstruction unit is used for reconstructing a three-dimensional image based on the target liver image and outputting a reconstruction result.
10. The liver image reconstruction system of claim 9, further comprising:
and the user database is used for storing the reconstruction result, and the file format of the reconstruction result in the user database is the nrrd format.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211079340.9A CN115409819B (en) | 2022-09-05 | 2022-09-05 | Liver image reconstruction method and reconstruction system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211079340.9A CN115409819B (en) | 2022-09-05 | 2022-09-05 | Liver image reconstruction method and reconstruction system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115409819A true CN115409819A (en) | 2022-11-29 |
| CN115409819B CN115409819B (en) | 2024-03-29 |
Family
ID=84164804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211079340.9A Active CN115409819B (en) | 2022-09-05 | 2022-09-05 | Liver image reconstruction method and reconstruction system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115409819B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738701A (en) * | 2019-10-23 | 2020-01-31 | 左丙丽 | tumor three-dimensional positioning system |
| CN111414923A (en) * | 2020-03-05 | 2020-07-14 | 南昌航空大学 | Indoor scene three-dimensional reconstruction method and system based on single RGB image |
| CN113643310A (en) * | 2021-05-21 | 2021-11-12 | 北京工业大学 | Context polymerization-based MRI image hepatic vessel segmentation method |
| WO2021232941A1 (en) * | 2020-05-18 | 2021-11-25 | 商汤集团有限公司 | Three-dimensional model generation method and apparatus, and computer device and storage medium |
| WO2021244621A1 (en) * | 2020-06-04 | 2021-12-09 | 华为技术有限公司 | Scenario semantic parsing method based on global guidance selective context network |
| CN113935976A (en) * | 2021-10-21 | 2022-01-14 | 西安交通大学医学院第二附属医院 | Method and system for automatically segmenting blood vessels in internal organs by enhancing CT (computed tomography) image |
| CN114612479A (en) * | 2022-02-09 | 2022-06-10 | 苏州大学 | Medical image segmentation method based on global and local feature reconstruction network |
| CN114972622A (en) * | 2021-12-30 | 2022-08-30 | 昆明理工大学 | High-precision three-dimensional reconstruction method for national clothing image |
-
2022
- 2022-09-05 CN CN202211079340.9A patent/CN115409819B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110738701A (en) * | 2019-10-23 | 2020-01-31 | 左丙丽 | tumor three-dimensional positioning system |
| CN111414923A (en) * | 2020-03-05 | 2020-07-14 | 南昌航空大学 | Indoor scene three-dimensional reconstruction method and system based on single RGB image |
| WO2021232941A1 (en) * | 2020-05-18 | 2021-11-25 | 商汤集团有限公司 | Three-dimensional model generation method and apparatus, and computer device and storage medium |
| WO2021244621A1 (en) * | 2020-06-04 | 2021-12-09 | 华为技术有限公司 | Scenario semantic parsing method based on global guidance selective context network |
| CN113643310A (en) * | 2021-05-21 | 2021-11-12 | 北京工业大学 | Context polymerization-based MRI image hepatic vessel segmentation method |
| CN113935976A (en) * | 2021-10-21 | 2022-01-14 | 西安交通大学医学院第二附属医院 | Method and system for automatically segmenting blood vessels in internal organs by enhancing CT (computed tomography) image |
| CN114972622A (en) * | 2021-12-30 | 2022-08-30 | 昆明理工大学 | High-precision three-dimensional reconstruction method for national clothing image |
| CN114612479A (en) * | 2022-02-09 | 2022-06-10 | 苏州大学 | Medical image segmentation method based on global and local feature reconstruction network |
Non-Patent Citations (2)
| Title |
|---|
| XIANGZHI BAI等: "Image fusion through local feature extraction by using multi-scale top-hat by reconstruction operators", 《OPTIK》 * |
| 张学昕等: "3D重建后平板CT在肝肿瘤介入治疗中的意义", 《医疗卫生装备》, no. 04 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115409819B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108520519B (en) | Image processing method and device and computer readable storage medium | |
| US7840044B2 (en) | Method and system for liver lobe segmentation and pre-operative surgical planning | |
| CN113506334B (en) | Multi-mode medical image fusion method and system based on deep learning | |
| CN108292435A (en) | The spinal vertebrae positioning and segmentation based on study in 3D CT | |
| CN111696089A (en) | Arteriovenous determining method, device, equipment and storage medium | |
| CN111598875A (en) | Method, system and device for building thyroid nodule automatic detection model | |
| CN111145173A (en) | Plaque identification method, device, equipment and medium for coronary angiography image | |
| CN113012155A (en) | Bone segmentation method in hip image, electronic device, and storage medium | |
| CN111178420B (en) | Coronary artery segment marking method and system on two-dimensional contrast image | |
| CN112348883B (en) | Interventional instrument endpoint real-time positioning system, method and device in vascular interventional operation | |
| CN114332577A (en) | Colorectal cancer image classification method and system combining deep learning and image omics | |
| CN110648318A (en) | Auxiliary analysis method and device for skin diseases, electronic equipment and storage medium | |
| CN111815592A (en) | Training method of pulmonary nodule detection model | |
| CN115761365A (en) | Intraoperative hemorrhage condition determination method and device and electronic equipment | |
| CN111401102B (en) | Deep learning model training method and device, electronic equipment and storage medium | |
| CN113033581B (en) | Bone anatomy key point positioning method in hip joint image, electronic equipment and medium | |
| Pham et al. | Chest x-rays abnormalities localization and classification using an ensemble framework of deep convolutional neural networks | |
| Jain et al. | Coinnet: A convolution-involution network with a novel statistical attention for automatic polyp segmentation | |
| CN116779093B (en) | Method and device for generating medical image structured report and computer equipment | |
| CN115409819A (en) | Liver image reconstruction method and reconstruction system | |
| CN117237351A (en) | Ultrasonic image analysis method and related device | |
| CN112347773A (en) | Medical application model training method and device based on BERT model | |
| CN112733953A (en) | Lung CT image arteriovenous vessel separation method based on Non-local CNN-GCN and topological subgraph | |
| CN116580801A (en) | Ultrasonic inspection method based on large language model | |
| CN114155232A (en) | Intracranial hemorrhage area detection method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240224 Address after: Room 603, No. 512 Yunchuang Road, Jiangling Street, Wujiang District, Suzhou City, Jiangsu Province, 215000 Applicant after: Suzhou Amimede Medical Technology Co.,Ltd. Country or region after: China Address before: 266100 Jufeng Venture Building 905, No. 52, Miaoling Road, Laoshan District, Qingdao, Shandong Applicant before: Qingdao emibochuang Medical Technology Co.,Ltd. Country or region before: China |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |