CN111477324A - Method, device, equipment and storage medium for emphysema disease prediction - Google Patents

Method, device, equipment and storage medium for emphysema disease prediction Download PDF

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CN111477324A
CN111477324A CN202010259591.XA CN202010259591A CN111477324A CN 111477324 A CN111477324 A CN 111477324A CN 202010259591 A CN202010259591 A CN 202010259591A CN 111477324 A CN111477324 A CN 111477324A
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emphysema
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李月
蔡杭
魏征
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WeBank Co Ltd
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Abstract

The invention discloses a method, a device, equipment and a storage medium for predicting emphysema diseases, wherein the method comprises the following steps: acquiring a single lobe image of the left or right lung; identifying emphysema regions and small airway lesion regions of the single lung lobe image; determining an emphysema grade detected based on the single lung lobe image, and determining a ratio of the emphysema region to the small airway lesion region; determining a predicted development level of emphysema based on the detected emphysema level and the ratio. The invention can determine the predicted development grade of the emphysema and predict the development direction of the emphysema diseases.

Description

Method, device, equipment and storage medium for emphysema disease prediction
Technical Field
The invention relates to the technical field of financial science and technology and artificial intelligence, in particular to a method, a device, equipment and a storage medium for emphysema disease prediction.
Background
Emphysema disease is a common condition that can be prevented and treated, characterized by persistent respiratory symptoms and restricted airflow, caused by airway and/or alveolar abnormalities resulting from significant exposure to harmful particles or gases.
Small bronchioles with an internal diameter of less than 2mm are commonly referred to clinically as small airways. The small air duct has the characteristics of small air flow resistance and easy blockage. During quiet inspiration, air enters the narrow nasopharynx, creating a vortex. Because the small airway is not supported by cartilage, after the small airway is separated from the fibrous sheath and embedded into lung tissue, the patency of the lumen is not like that of a chondral airway and is easily influenced by the pressure change of the thoracic cavity.
The global initiative for pulmonary emphysema based on sexual obstructive disease (GO L D), levels for emphysema include GO L D grade 0 (non-existent), GO L D grade 1 (mild), GO L D grade 2 (moderate), GO L D grade 3 (severe) and GO L D grade 4 (extreme).
At present, the grade of the emphysema disease can be detected only by images, but the development direction of the emphysema disease cannot be predicted.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for predicting emphysema diseases, and aims to solve the problem that the development direction of emphysema diseases cannot be predicted at present.
In order to achieve the above object, the present invention provides a method for predicting an emphysema disease, comprising:
acquiring a single lobe image of the left or right lung;
identifying emphysema regions and small airway lesion regions of the single lung lobe image;
determining an emphysema grade detected based on the single lung lobe image, and determining a ratio of the emphysema region to the small airway lesion region;
determining a predicted development level of emphysema based on the detected emphysema level and the ratio.
Optionally, the determining a ratio of the emphysema region to the small airway lesion region comprises:
respectively determining the volume of an emphysema region and the volume of a small airway lesion region according to the emphysema region and the small airway lesion region;
determining the ratio of the emphysema region to the small airway lesion region based on the emphysema region volume and the small airway lesion region volume.
Optionally, the determining, according to the emphysema region and the small airway lesion region, an emphysema region volume and a small airway lesion region volume respectively includes:
determining the number of layers, the scanning layer thickness and the layer spacing of the single lung lobe image;
gridding the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region;
determining the emphysema region volume based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area;
and determining the volume of the small airway lesion area based on the number of layers, the scanning layer thickness, the layer spacing and the second grid area.
Optionally, the grid processing is performed on the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region, and the grid processing includes:
determining an edge of the emphysema region;
respectively drawing grids in the emphysema region, and thinning the grids when the drawn grids contact the edges;
stopping the thinning processing when the number of the grids is greater than or equal to a first preset number, and determining the sum of the areas of the grids as a first gridding area of the edge of the emphysema area;
and performing gridding processing on the small airway lesion area, which is the same as the emphysema area, to obtain a second grid area of the small airway lesion area.
Optionally, the identifying emphysema regions and small airway lesion regions of the single lung lobe image includes:
extracting lung parenchyma of the single lung lobe image;
determining the emphysema region and the small airway lesion region based on the lung parenchyma.
Optionally, said determining a predicted level of development of emphysema based on said detected emphysema level and said ratio comprises:
optionally, if the ratio is greater than a preset threshold, determining a predicted development level of emphysema as a next level of the detected emphysema level;
if the ratio is smaller than or equal to a preset threshold value, determining the predicted development level of the emphysema as the previous level of the detected emphysema level; wherein the last level is more severe than the detected emphysema level.
Optionally, the acquiring a single lobe image of the left or right lung comprises:
acquiring a lung image;
segmenting the lung image to obtain a lung lobe segmentation image;
extracting the single lung lobe image from the lung lobe segmentation image.
In a second aspect, the present invention provides an emphysema disease prediction apparatus including:
the acquisition module is used for acquiring a single lung lobe image of the left lung or the right lung;
the identification module is used for identifying an emphysema region and a small airway lesion region of the single lung lobe image;
a first determination module, configured to determine an emphysema grade detected based on the single lung lobe image, and determine a ratio of the emphysema region to the small airway lesion region;
a second determination module for determining a predicted development level of emphysema based on the detected emphysema level and the ratio.
Optionally, the first determining module includes:
the first determining unit is used for respectively determining the volume of the emphysema area and the volume of the small airway lesion area according to the emphysema area and the small airway lesion area;
a second determining unit, configured to determine a ratio of the emphysema region to the small airway lesion region based on the emphysema region volume and the small airway lesion region volume.
Optionally, the first determining unit is specifically configured to determine the number of layers, the scanning layer thickness, and the layer spacing of the single lung lobe image;
gridding the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region;
determining the emphysema region volume based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area;
and determining the volume of the small airway lesion area based on the number of layers, the scanning layer thickness, the layer spacing and the second grid area.
Optionally, the first determining unit is specifically further configured to determine an edge of the emphysema region;
respectively drawing grids in the emphysema region, and thinning the grids when the drawn grids contact the edges;
stopping the thinning processing when the number of the grids is greater than or equal to a first preset number, and determining the sum of the areas of the grids as a first gridding area of the edge of the emphysema area;
and performing gridding processing on the small airway lesion area, which is the same as the emphysema area, to obtain a second grid area of the small airway lesion area.
Optionally, the identification unit is configured to extract lung parenchyma of the single lung lobe image; and determining the emphysema region and the small airway lesion region based on the lung parenchyma.
Optionally, the second determining module is specifically configured to determine, if the ratio is greater than a preset threshold, a predicted development level of emphysema as a next level of the detected emphysema level;
if the ratio is smaller than or equal to a preset threshold value, determining the predicted development level of the emphysema as the previous level of the detected emphysema level; wherein the last level is more severe than the detected emphysema level.
Optionally, the acquiring module is specifically configured to acquire a lung image; segmenting the lung image to obtain a lung lobe segmentation image; and extracting the single lung lobe image from the lung lobe segmentation image.
In a third aspect, the present invention further provides an emphysema disease prediction apparatus, including: the system comprises a memory, a processor and an emphysema disease prediction program stored on the memory and capable of running on the processor, wherein the emphysema disease prediction program is executed by the processor to perform the steps of the emphysema disease prediction method.
In a fourth aspect, the present invention further provides a computer-readable storage medium, in which an emphysema disease prediction program is stored, and when the emphysema disease prediction program is executed by a processor, the steps of the emphysema disease prediction method are implemented.
Compared with the situation that the prediction development of the emphysema of the left lung or the right lung cannot be predicted in the prior art, the method, the device, the equipment and the storage medium for predicting the emphysema disease are implemented after a single lung lobe image of the left lung or the right lung is obtained; emphysema regions and small airway lesion regions of the single lung lobe image can be identified; determining an emphysema grade detected based on the single lung lobe image, and determining a ratio of the emphysema region to the small airway lesion region; based on the detected emphysema grade and the ratio, the predicted development grade of the emphysema can be determined, namely the development direction of the emphysema disease can be predicted.
Drawings
FIG. 1 is a schematic diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for emphysema disease prediction according to an embodiment of the present invention;
FIG. 3 is a block diagram of a functional diagram of an emphysema disease prediction device in accordance with a preferred embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
It should be noted that the emphysema disease prediction method provided in the embodiment of the present invention may be used to improve detection efficiency and accuracy of emphysema grade, and an execution main body of the method may be any emphysema disease prediction apparatus, for example, the emphysema disease prediction method may be executed by a terminal device, a server, or other processing devices, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, and the like, and is not limited herein.
As shown in fig. 1, the emphysema disease prediction apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the device configuration shown in fig. 1 does not constitute a limitation of the emphysema disease prediction device, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include an operating system, a network communication module, a user interface module, and an emphysema disease prediction program therein. The operating system is a program for managing and controlling hardware and software resources of the equipment, and supports the operation of the emphysema disease prediction program and other software or programs.
In the device shown in fig. 1, the user interface 1003 is mainly used for data communication with a client; the network interface 1004 is mainly used for establishing communication connection with a server; the processor 1001 may be configured to call the emphysema disease prediction program stored in the memory 1005, and execute the emphysema disease prediction method according to the embodiment of the present invention.
To achieve the objective of predicting the development trend of emphysema diseases, an embodiment of the invention provides a method for predicting emphysema diseases, as shown in fig. 2, which includes the following steps.
S10: acquiring a single lobe image of the left or right lung;
s20: identifying emphysema regions and small airway lesion regions of the single lung lobe image;
s30: determining an emphysema grade detected based on the single lung lobe image, and determining a ratio of the emphysema region to the small airway lesion region;
s40: determining a predicted development level of emphysema based on the detected emphysema level and the ratio.
In some possible implementations, the lung image according to the embodiment of the present invention may be obtained by capturing a CT (computed tomography). Wherein if the single lung lobe image is of the left lung, the single lung lobe image may be of the upper left lobe or the lower left lobe. If the single lung lobe image is of the right lung, the single lung lobe image may be of the upper right lobe, the middle right lobe, and the lower right lobe.
It should be noted that, in order to acquire a single lung lobe image of the left lung or the right lung, the method includes: acquiring a lung image; segmenting the lung image to obtain a lung lobe segmentation image; extracting the single lung lobe image from the lung lobe segmentation image. Specifically, a lung CT image is acquired, the CT image comprising: full inspiratory lung images and full expiratory lung images. Respectively segmenting or extracting the full inhalation phase lung image and the full exhalation phase lung image to obtain a first left lung segmentation image, a first right lung segmentation image, a second left lung segmentation image and a second right lung segmentation image; wherein the left lung image comprises: the first left lung segmentation image and the second left lung segmentation image; wherein the right lung image comprises: the first right lung segmentation image and the second right lung segmentation image. Namely, segmenting or extracting the full inhalation phase lung image to obtain a first left lung segmentation image and a first right lung segmentation image; and segmenting or extracting the full respiratory phase lung image to obtain a second left lung segmentation image and a second right lung segmentation image. The first left lung segmentation image and the second left lung segmentation image are left lung segmentation images of a full inhalation phase and a full exhalation phase respectively. The first right lung segmentation image and the second right lung segmentation image are right lung segmentation images of a full inspiratory phase and a full expiratory phase respectively.
In an embodiment of the present invention, in order to identify an emphysema region and a small airway lesion region of the single lung lobe image, the step S20 includes: extracting lung parenchyma of the single lung lobe image; determining the emphysema region and the small airway lesion region based on the lung parenchyma.
Specifically, the left lung parenchyma of the left lung image may be extracted, or the right lung parenchyma of the right lung image may be extracted; an emphysema region and the small airway lesion region of the left lung are then determined at the left lung parenchyma, or an emphysema region and a small airway lesion region of the right lung are then determined at the right lung parenchyma, respectively. In the embodiment of the present invention, the purpose of extracting the left lung parenchyma of the left lung image and the right lung parenchyma of the right lung image is to remove the trachea and blood vessels of the left lung image and the right lung image, so as to ensure the accuracy of emphysema disease prediction.
In the embodiment of the invention, the emphysema region and the small airway lesion region are not easy to determine on the image, and particularly the small airway lesion region is not obvious on the image. The method for identifying the emphysema regions of the left lung image and the right lung image comprises the following steps: and judging whether the CT value on the lung lobe is an emphysema region or not according to the CT values of the first left lung segmentation image and the second left lung segmentation image and an emphysema set threshold value. The emphysema setting threshold is a set CT value, the CT value of an emphysema region is basically unchanged when deep inhalation is carried out, other normal regions (non-emphysema regions) enter air, the CT value of the air is 1024HU, and the emphysema region is filled with air, so that the CT value of the emphysema region is close to 1024 HU. In the medical field, the emphysema setting threshold is generally selected as-950 HU, and if the CT value of the lobe is less than-950 HU, the emphysema region is judged, and if the CT value of the lobe is greater than or equal to-950 HU, the emphysema region is judged not to be the emphysema region. In other published papers or in some possible embodiments, the set threshold may fluctuate, and the present invention does not specifically limit the set threshold, and the person skilled in the art can appropriately adjust the set threshold for emphysema.
In an embodiment of the present invention, the method for identifying the small airway lesion regions of the left lung image and the right lung image comprises: identifying small airway lesion regions of the left lung image and the right lung image requires a full inspiratory phase lung image and a full expiratory phase lung image. The method for identifying the small airway lesion areas of the left lung image and the right lung image comprises the following steps: and acquiring the full inhalation phase lung image, a first left lung segmentation image and a first right lung segmentation image which are obtained by segmenting the full exhalation phase lung image, and a second left lung segmentation image and a second right lung segmentation image.
The method for identifying the small airway lesion region of the left lung image comprises the following steps: registering a first left lung segmentation image of the full inspiratory lung image with a second left lung segmentation image of the full expiratory lung image. Comparing the registered first left lung segmentation image and the registered second left lung segmentation image with an inhalation phase set threshold and an exhalation phase set threshold respectively; if the CT value of the first left lung segmentation image of the registered full inhalation-phase lung image is smaller than the inhalation-phase set threshold value and the CT value of the second left lung segmentation image of the registered full exhalation-phase lung image is smaller than the exhalation-phase set threshold value, determining that the region has small airway lesion, and the region is a small airway lesion region; otherwise, the region is not considered to have small airway lesions, and the region is not the small airway lesion region.
The method for identifying the small airway lesion region of the right lung image comprises the following steps: registering a first right lung segmentation image of the full inspiratory phase lung image with a second right lung segmentation image of the full expiratory phase lung image. Comparing the registered first right lung segmentation image and the registered second right lung segmentation image with an inhalation phase set threshold and an exhalation phase set threshold respectively; if the CT value of the first right lung segmentation image of the registered full inhalation-phase lung image is smaller than the inhalation-phase set threshold value and the CT value of the second right lung segmentation image of the registered full exhalation-phase lung image is smaller than the exhalation-phase set threshold value, determining that the region has small airway lesion, and the region is a small airway lesion region; otherwise, the region is not considered to have small airway lesions, and the region is not the small airway lesion region.
In the embodiment of the invention, the full inhalation phase lung image and the full exhalation phase lung image are the lung images of a patient, and the lung images shot by the influence equipment are utilized when the air volume of the lung is kept to be maximum under deep inhalation during the full inhalation phase lung image. Similarly, the full expiratory phase lung image is a lung image shot by using the influence equipment when the air volume of the lung is kept to be minimum under deep expiration. Full inspiratory and expiratory phase pulmonary images are available to imaging physicians at hospitals by means of imaging devices (e.g., CT).
It should be noted that, the algorithm for registering the first left lung segmentation image of the full inhalation phase lung image and the second left lung segmentation image of the full exhalation phase lung image may use an elastic registration algorithm or perform registration by using a VGG network (VGG-net) in deep learning, and the embodiment of the present invention does not limit a specific registration algorithm.
In an embodiment of the present invention, the inspiratory phase setting threshold may be set to-950 HU and the expiratory phase setting threshold may be set to-856 HU. In other published papers or in some possible embodiments, the inspiratory phase setting threshold and the expiratory phase setting threshold may fluctuate, and the present invention does not specifically limit the setting of the thresholds, and the skilled person can appropriately adjust the inspiratory phase setting threshold and the expiratory phase setting threshold.
In the embodiment of the invention, according to the global initiative for chronic obstructive pulmonary disease (emphysema) (GO L D), emphysema grades comprise GO L D0 grade (non-existing), GO L D1 grade (mild), GO L D2 grade (moderate), GO L D3 grade (severe) and GO L D4 grade (extreme weight). The emphysema grades of the left lung image and the right lung image can be determined by the above configuration.
That is, for step S30, the determining the emphysema grade detected based on the single lung lobe image includes: and inputting the single lung lobe image into a preset neural network to extract image characteristics through a residual error network of the preset neural network, and determining the emphysema grade detected based on the single lung lobe image according to a classification network of the preset neural network.
In an embodiment of the present invention, the emphysema grade detected based on the single lung lobe image may also be determined by the following method, including: determining an emphysema region volume of the single lung lobe image and a total volume of the single lung lobe image, and determining an emphysema grade of the single lung lobe image according to a ratio of the emphysema region volume of the single lung lobe image and the total volume of the single lung lobe image. The method specifically comprises the following steps: determining the volume of an emphysema region of the left lung image and the total volume of the left lung image, and determining the emphysema grade of the left lung image according to the ratio of the volume of the emphysema region of the left lung image to the total volume of the left lung image; or determining the volume of the emphysema region of the right lung image and the total volume of the right lung image, and determining the grade of the right lung image according to the ratio of the volume of the emphysema region of the right lung image to the total volume of the right lung image.
Here, the description is given by taking an example of determining the emphysema grade of a left lung image from the left lung image, and if four thresholds are set, namely a first threshold, a second threshold, a third threshold and a fourth threshold, and if the ratio of the volume of the emphysema region of the left lung image to the total volume of the left lung image is smaller than the first threshold, the emphysema grade is GO L D0 (no emphysema), if the ratio of the volume of the emphysema region of the left lung image to the total volume of the left lung image is between the first threshold and the second threshold, the emphysema grade is GO L D1 (mild), if the ratio of the volume of the emphysema region of the left lung image to the total volume of the left lung image is between the second threshold and the third threshold, the emphysema grade is GO L D2 (moderate emphysema), if the ratio of the volume of the emphysema region of the left lung image to the total volume of the left lung image is between the third threshold and the fourth threshold, the emphysema grade is L D3 (lung image), if the ratio of the volume of the emphysema region of the left lung image to the left lung image is greater than the third lung image, the fourth threshold, the emphysema grade no emphysema grade GO 80D 82, and the fourth threshold, the ratio of the emphysema grade is determined according to the fourth lung image, the fourth threshold, the ratio of the emphysema grade is determined according to the lung image, the ratio of the lung threshold, the lung image is 30% of the lung image, and the fourth threshold, and the fourth lung threshold, wherein the fourth lung threshold, the second threshold, the.
In an embodiment of the present invention, for step S30, the determining a ratio of the emphysema region and the small airway lesion region includes: respectively determining the volume of an emphysema region and the volume of a small airway lesion region according to the emphysema region and the small airway lesion region; determining the ratio of the emphysema region to the small airway lesion region based on the emphysema region volume and the small airway lesion region volume.
It should be noted that the determining, according to the emphysema region and the small airway lesion region, an emphysema region volume and a small airway lesion region volume respectively includes: determining the number of layers, the scanning layer thickness and the layer spacing of the single lung lobe image; gridding the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region; determining the emphysema region volume based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area; and determining the volume of the small airway lesion area based on the number of layers, the scanning layer thickness, the layer spacing and the second grid area.
Specifically, the single lobe image of the left lung includes: the method for determining the emphysema region volume and the small airway lesion region volume according to the emphysema region and the small airway lesion region comprises the following steps: determining the number of layers, the scanning layer thickness and the interlayer spacing of the full inhalation-phase lung image of the left lung or the full exhalation-phase lung image of the left lung; and gridding the emphysema region of each layer of left lung and the small airway lesion region of each layer of left lung to obtain the emphysema region grid area and the small airway lesion region grid area of each layer of left lung, and determining the emphysema region volume of the left lung and the small airway lesion region volume of the left lung according to the emphysema region grid area of each layer of left lung and the small airway lesion region grid area, the number of layers, the scanning layer thickness and the layer spacing respectively.
Alternatively, the single lobe image of the right lung includes: the determining of the emphysema region volume and the small airway lesion region volume according to the emphysema region and the small airway lesion region respectively comprises: determining the number of layers, the scanning layer thickness and the interlayer spacing of the full inhalation-phase lung image of the right lung or the full exhalation-phase lung image of the right lung; and gridding the emphysema region of each layer of right lung and the small airway lesion region of each layer of right lung to obtain the emphysema region grid area and the small airway lesion region grid area of each layer of right lung, and determining the emphysema region volume of the right lung and the small airway lesion region volume of the right lung according to the emphysema region grid area of each layer of left lung and the small airway lesion region grid area, the number of layers, the scanning layer thickness and the layer spacing respectively. The number of layers, the scanning layer thickness and the layer spacing of the full inhalation phase lung image and the full exhalation phase lung image are the same.
In an embodiment of the present invention, the gridding the emphysema region and the small airway lesion region to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region includes: determining an edge of the emphysema region; respectively drawing grids in the emphysema region, and thinning the grids when the drawn grids contact the edges; stopping the thinning processing when the number of the grids is greater than or equal to a first preset number, and determining the sum of the areas of the grids as a first gridding area of the edge of the emphysema area; and performing gridding processing on the small airway lesion area, which is the same as the emphysema area, to obtain a second grid area of the small airway lesion area.
It should be noted that the drawing of the mesh in the emphysema region, and when the drawn mesh touches the edge, performing refinement processing on the mesh, includes: drawing a first specification shape grid in the emphysema region, and extending the first specification shape grid to the edge; stopping extending the first form grid when the set points of the first form grid contact the edge, and generating a second form grid outside the first form grid, wherein the set points and areas of the second form grid are less than the set points and areas of the first form grid, respectively; performing an extension process on the second specification shape mesh toward the edge.
For example, if the first gauge shape is selected to be circular, several set points, such as 20 set points, can be uniformly set on the circumference of the circle, and when the several set points of the first gauge shape contact the edge of the small airway diseased region, the first gauge shape stops extending and generates a second gauge shape outside the gauge shape; the second specification shape can be any one of a circle, an ellipse, a rectangle and a square, but the second specification shape is smaller than the first specification shape to achieve the purpose of fine division. For the setting of the quantity value of the set quantity, the skilled person can set the quantity value according to the precision requirement.
Specifically, the grid processing may be performed on the emphysema region of each left lung or right lung layer to obtain a first grid area of the emphysema region of each left lung or right lung layer, and the grid processing may be performed on the small airway lesion region of each left lung or right lung layer to obtain a second grid area of the small airway lesion region. The gridding processing is performed on the emphysema region of each layer of left lung or right lung to obtain a first grid area of the emphysema region of each layer of left lung or right lung, and the gridding processing comprises the following steps: step 1: determining the emphysema region edge of each layer of the left lung or the right lung respectively; determining a first specification shape area in the emphysema area of each layer of the left lung or the right lung respectively; step 2: the first specification shape is towards the emphysema region edge of the left lung or the right lung of each layer; and step 3: when a plurality of set points of the specification shapes contact the emphysema region edge of the left lung or the right lung of each layer, the first specification shape stops extending, and a second specification shape is generated outside the specification shapes; sequentially generating according to the steps 2 and 3 until the number of the specification shapes reaches the set number, and calculating the areas of all the specification shapes to obtain the first grid area of the emphysema area of the left lung or the right lung of each layer; and the specification shapes, the second specification shapes and the specification shape areas corresponding to the set number are reduced in sequence. Similarly, the first network area of each layer may be obtained by performing a gridding process on the small airway lesion area of the left lung or the right lung of each layer to obtain a second grid area of the small airway lesion area of the left lung or the right lung of each layer.
For the embodiment of the present invention, in order to ensure accuracy of a first grid area of an emphysema region, the first specification shape grid may be drawn from a geometric center of the emphysema region, that is, the first specification shape grid is located at the geometric center of the emphysema region, and before the first specification shape grid is drawn in the emphysema region, the method further includes: acquiring a plurality of reference points arranged in the emphysema region; calculating a plurality of distances from each reference point to the edge respectively; and determining the geometric center of the emphysema region according to the distances. Likewise, a grid may be drawn from the geometric center of the small airway lesion region and the geometric center of the small airway lesion region determined.
In an embodiment of the present invention, the specific method for calculating the distances from the points to the edge respectively to obtain the distances from each point to the edge includes: respectively taking the points as centers, emitting towards the edge in a mode of a plurality of rays, marking when the rays reach the edge or intersect with the edge, and making a plurality of marks, wherein the distance from each point to each mark is the distance from each point to the edge. Wherein, the plurality of rays can be equally spaced or unequally spaced.
If the points are 10 points, the positions of the 10 points are in the emphysema region, 10 points are respectively calculated as the center, the points are emitted to the edge in a mode of a plurality of rays (100 rays), and a plurality of marks (100 marks) are made when the rays reach the edge or intersect with the edge; the distance between each point to the number of marks is a number of distances from each point to the edge.
Specifically, a certain point is defined as OX, X represents the nth point, X ═ 1 represents the first point of 10 points, and a certain point emits 100 rays toward the edge, and the number of the markers is 100, namely X1 to X100; the number of the distances from X1 to X100 on the edge from a certain point OX is 100, and the geometric center of the small airway lesion area is determined according to the 100 distances from each point in the 10 points.
In the embodiment of the present invention, the method of specifying the geometric center of the emphysema region is the same as the method of specifying the geometric center of the small airway lesion region, and the method of specifying the geometric center of the emphysema region will be described as an example.
Determining a geometric center of the emphysema region according to the plurality of distances, including: respectively calculating the difference values among the distances; counting the number of the distance difference values corresponding to the reference points which are smaller than a preset threshold value; and determining the positions of the reference points with the number larger than or equal to the preset number as the geometric center of the emphysema region. Specifically, the difference between a plurality of distances from each point to the edge is calculated respectively; comparing the difference value between the distances of a certain point with a set value, and if the difference value is smaller than the set value, accumulating; and when the accumulated number is greater than or equal to the preset number, the position of the certain point is the geometric center of the small airway lesion area.
For example, taking one of the points as an example, 100 rays with this point as the center are emitted to the edge, the number of distances from this point to the edge is 100, the difference between 100 rays is calculated, each difference is compared with a set value, and if the difference is smaller than the set value, the difference is accumulated; and when the accumulated number is greater than or equal to the preset number, the position of the point is the geometric center of the emphysema region. For the setting of the set value and the preset number, a person in the art can set according to the number of distances from the point to the edge and the precision requirement, for example, the set value can be 0.5-1 mm; when the number of the distances from the point to the edge is 100, the predetermined number may be 80.
In an embodiment of the present invention, if the positions of a plurality of points (e.g., 2 points) are all determined as the geometric center of the emphysema region, the cumulative number of the plurality of points is compared, and the cumulative number is the geometric center of the small airway lesion region.
And if the plurality of points are 2 points, the points are respectively a first point and a second point, if the positions of the first point and the second point are both determined to be the geometric center of the emphysema region, comparing the accumulated number of the first point with the accumulated number of the second point, and if the accumulated number of the first point is greater than the accumulated number of the second point, the position of the first point is the geometric center of the emphysema region.
In an embodiment of the present invention, the specific method for determining the geometric center of the small airway lesion area according to the distances from each point to the edge is as follows: respectively calculating the difference between a plurality of distances from each point to the edge; comparing the difference value between the distances of a certain point with a set value, and if the difference value is smaller than the set value, accumulating; and when the accumulated quantity is greater than or equal to the preset quantity, the position of the certain point is the geometric center of the emphysema region.
In other embodiments of the present invention, the method for determining the geometric center of the emphysema region may further include: reducing the edge of the emphysema region according to a first preset scale to obtain a first edge; selecting a plurality of first reference points on the first edge, and fitting by using the first reference points to form a first circle; in response to the fact that the radius of the first circle is larger than a radius threshold, carrying out reduction processing on the first edge according to a second preset scale until the radius of a circle formed by a plurality of second reference points selected from the reduced edge is smaller than the radius threshold; and determining the geometric center of the small airway lesion area by using the position mean value of the second reference points or the circle center of a circle formed by the second reference points. The first preset size is larger than the second preset size, for example, the first preset size may be 80%, and the second preset size may be 10%, but is not limited to the specific limitations of the embodiments of the present invention.
That is, in the embodiment of the present invention, when the emphysema region is identified, the edge of the emphysema region may be identified to form an edge. The edge may then be scaled down by a first predetermined scale to obtain a first edge. Then, a plurality of first reference points may be selected from the first edge according to a first rule, where an edge of the emphysema region may be regarded as being formed by a plurality of points, and the corresponding first edge may also include a plurality of points, and the first rule may include selecting a plurality of first reference points according to a preset interval point number, for example, the preset interval point number may be 10, and then selecting one first reference point every 10 points on the first edge, where the preset interval point number is not specifically limited. After the first reference point is selected, a curve fitting may be performed on the first reference point to form a first circle, and then a radius of the first circle is determined, wherein the method of curve fitting may be a least square method, but is not limited by the present invention. If the radius of the first circle is smaller than or equal to the radius threshold, the center of the first circle may be used as the geometric center of the small airway lesion area, or the mean position of the first reference point may also be determined as the geometric center of the small airway lesion area.
If the radius of the first circle is larger than the radius threshold, it indicates that the first edge needs to be further narrowed, so as to reduce the error, and at this time, the narrowing may be performed according to a second preset scale, where the second preset scale is smaller than the first preset scale. When the first edge is reduced to form the second edge (at least one), a plurality of second reference points may be selected on the second edge, and similarly, a circle (second circle) may be fitted by using the second reference points, and it is determined whether reduction of the second edge is required according to the above rule. If the radius of the second circle fitted by the second reference point is larger than the radius threshold, the second edge is further narrowed according to a second preset scale, otherwise, if the radius of the second circle is smaller than or equal to the radius threshold, the center of the second circle or the position mean value of the second reference point can be determined as the geometric center of the emphysema region.
In an embodiment of the present invention, the determining the volume of the small airway lesion region based on the number of layers, the scanning layer thickness, the layer interval, and the grid area includes: obtaining subvolumes formed by the two adjacent layers of small airway lesion areas by utilizing the grid areas of the two adjacent layers of small airway lesion areas, the scanning layer thickness and the interlayer spacing; and calculating the sum of the sub-volumes corresponding to the number of layers to obtain the volume of the small airway lesion area.
Specifically, the sub-volumes formed by the two adjacent layers of small airway lesion areas are obtained by utilizing the corresponding grid area, the interlayer spacing and the layer thickness of each two adjacent layers of small airway lesion areas, and the volume of each small airway lesion area is obtained by utilizing the sum of the sub-volumes formed by all the two adjacent layers of small airway lesion areas. The structure formed by two adjacent layers of regions satisfying the lung can be regarded as a prismatic table, the areas of the upper bottom surface and the lower bottom surface of the prismatic table are the grid areas corresponding to the small airway lesion regions, the height of the prismatic table can be determined by the layer spacing and the layer thickness, for example, the number of layers is N, the height of the prismatic table formed by the first layer and the second layer of the small airway lesion regions can be the sum of the layer thickness 2 and the layer spacing, and the height of the prismatic table formed by the other two adjacent layers of the small airway lesion regions can be the sum of the layer thickness and the layer spacing. The subvolumes of the small airway lesion areas formed by adjacent layers can be determined based on the upper and lower floor areas and the height. The sum of the sub-volumes can then be used to derive the volume of the small airway lesion. That is, since there may be many small airway lesion regions, the small airway lesion region volume is the total volume of all small airway lesion regions.
Similarly, the manner of determining the volume of the emphysema region based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area is the same as the manner of determining the volume of the small airway lesion region, which is not described herein in detail in the embodiments of the present invention.
For the embodiment of the present invention, the step S40, determining the predicted development level of emphysema based on the detected emphysema level and the ratio includes: if the ratio is larger than a preset threshold value, determining the predicted development grade of the emphysema as the next grade of the detected emphysema grade; if the ratio is smaller than or equal to a preset threshold value, determining the predicted development level of the emphysema as the previous level of the detected emphysema level; wherein the last level is more severe than the detected emphysema level.
For example, on the left lung, the emphysema grade detected based on the image is GO L D2 grade (moderate), the volume of the emphysema region of the left lung is divided by the volume of the small airway lesion region of the left lung to obtain a ratio, the ratio is smaller than or equal to the preset threshold, which indicates that although the emphysema region of the left lung is small, the small airway lesion region is large, the probability of converting the small airway lesion region into the emphysema region is high, the emphysema grade of the left lung can be predicted to be GO L D3 grade (severe), the ratio is greater than the preset threshold, it is considered that the drug therapy can be performed, and the emphysema grade of the left lung can be predicted to be GO L D1 grade (mild), wherein the preset threshold can be 0.4.
In addition, an embodiment of the present invention further provides an emphysema disease prediction apparatus, where, with reference to fig. 3, the emphysema disease prediction apparatus includes:
an acquisition module 10, configured to acquire a single lung lobe image of a left lung or a right lung;
an identifying module 20, configured to identify an emphysema region and a small airway lesion region of the single lung lobe image;
a first determining module 30, configured to determine an emphysema grade detected based on the single lung lobe image, and determine a ratio of the emphysema region to the small airway lesion region;
a second determining module 40 for determining a predicted development level of emphysema based on said detected emphysema level and said ratio.
Optionally, the first determining module 10 includes:
the first determining unit is used for respectively determining the volume of the emphysema area and the volume of the small airway lesion area according to the emphysema area and the small airway lesion area;
a second determining unit, configured to determine a ratio of the emphysema region to the small airway lesion region based on the emphysema region volume and the small airway lesion region volume.
Optionally, the first determining unit is specifically configured to determine the number of layers, the scanning layer thickness, and the layer spacing of the single lung lobe image;
gridding the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region;
determining the emphysema region volume based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area;
and determining the volume of the small airway lesion area based on the number of layers, the scanning layer thickness, the layer spacing and the second grid area.
Optionally, the first determining unit is specifically further configured to determine an edge of the emphysema region;
respectively drawing grids in the emphysema region, and thinning the grids when the drawn grids contact the edges;
stopping the thinning processing when the number of the grids is greater than or equal to a first preset number, and determining the sum of the areas of the grids as a first gridding area of the edge of the emphysema area;
and performing gridding processing on the small airway lesion area, which is the same as the emphysema area, to obtain a second grid area of the small airway lesion area.
Optionally, the identification unit is configured to extract lung parenchyma of the single lung lobe image; and determining the emphysema region and the small airway lesion region based on the lung parenchyma.
Optionally, the second determining module 40 is specifically configured to determine, if the ratio is greater than a preset threshold, a predicted development level of emphysema as a next level of the detected emphysema level;
if the ratio is smaller than or equal to a preset threshold value, determining the predicted development level of the emphysema as the previous level of the detected emphysema level; wherein the last level is more severe than the detected emphysema level.
Optionally, the acquiring module 10 is specifically configured to acquire a lung image; segmenting the lung image to obtain a lung lobe segmentation image; and extracting the single lung lobe image from the lung lobe segmentation image.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, in which an emphysema disease prediction program is stored, and when the emphysema disease prediction program is executed by a processor, the steps of the emphysema disease prediction method are implemented as follows.
For the embodiments of the emphysema disease prediction apparatus and the computer-readable storage medium of the present invention, reference may be made to the embodiments of the emphysema disease prediction method of the present invention, and details thereof are not described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A method for predicting an emphysema disease, comprising:
acquiring a single lobe image of the left or right lung;
identifying emphysema regions and small airway lesion regions of the single lung lobe image;
determining an emphysema grade detected based on the single lung lobe image, and determining a ratio of the emphysema region to the small airway lesion region;
determining a predicted development level of emphysema based on the detected emphysema level and the ratio.
2. The method of claim 1, wherein determining the ratio of the emphysema region to the small airway lesion region comprises:
respectively determining the volume of an emphysema region and the volume of a small airway lesion region according to the emphysema region and the small airway lesion region;
determining the ratio of the emphysema region to the small airway lesion region based on the emphysema region volume and the small airway lesion region volume.
3. The method of claim 2, wherein determining an emphysema region volume and a small airway lesion volume from the emphysema region and the small airway lesion region, respectively, comprises:
determining the number of layers, the scanning layer thickness and the layer spacing of the single lung lobe image;
gridding the emphysema region and the small airway lesion region respectively to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region;
determining the emphysema region volume based on the number of layers, the scanning layer thickness, the layer spacing and the first grid area;
and determining the volume of the small airway lesion area based on the number of layers, the scanning layer thickness, the layer spacing and the second grid area.
4. The method of claim 3, wherein the gridding the emphysema region and the small airway lesion region to obtain a first grid area of the emphysema region and a second grid area of the small airway lesion region comprises:
determining an edge of the emphysema region;
respectively drawing grids in the emphysema region, and thinning the grids when the drawn grids contact the edges;
stopping the thinning processing when the number of the grids is greater than or equal to a first preset number, and determining the sum of the areas of the grids as a first gridding area of the edge of the emphysema area;
and performing gridding processing on the small airway lesion area, which is the same as the emphysema area, to obtain a second grid area of the small airway lesion area.
5. The method of claim 1, wherein the identifying emphysema and small airway lesion regions of the single lung lobe image comprises:
extracting lung parenchyma of the single lung lobe image;
determining the emphysema region and the small airway lesion region based on the lung parenchyma.
6. The method of claim 1, wherein said determining a predicted level of development of emphysema, based on said detected emphysema level and said ratio, comprises:
if the ratio is larger than a preset threshold value, determining the predicted development grade of the emphysema as the next grade of the detected emphysema grade;
if the ratio is smaller than or equal to a preset threshold value, determining the predicted development level of the emphysema as the previous level of the detected emphysema level; wherein the last level is more severe than the detected emphysema level.
7. The method of claim 1, wherein said acquiring a single lobe image of the left or right lung comprises:
acquiring a lung image;
segmenting the lung image to obtain a lung lobe segmentation image;
extracting the single lung lobe image from the lung lobe segmentation image.
8. An emphysema disease prediction apparatus comprising:
the acquisition module is used for acquiring a single lung lobe image of the left lung or the right lung;
the identification module is used for identifying an emphysema region and a small airway lesion region of the single lung lobe image;
a first determination module, configured to determine an emphysema grade detected based on the single lung lobe image, and determine a ratio of the emphysema region to the small airway lesion region;
a second determination module for determining a predicted development level of emphysema based on the detected emphysema level and the ratio.
9. An emphysema disease prediction apparatus characterized by comprising: memory, processor and an emphysema disease prediction program stored on the memory and executable on the processor, the emphysema disease prediction program when executed by the processor implementing the steps of the emphysema disease prediction method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon an emphysema disease prediction program which, when executed by a processor, implements the steps of the emphysema disease prediction method according to any one of claims 1 to 7.
CN202010259591.XA 2020-04-03 2020-04-03 Method, device, equipment and storage medium for emphysema disease prediction Pending CN111477324A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113409306A (en) * 2021-07-15 2021-09-17 推想医疗科技股份有限公司 Detection device, training method, training device, equipment and medium
CN114511562A (en) * 2022-04-19 2022-05-17 深圳市疾病预防控制中心（深圳市卫生检验中心、深圳市预防医学研究所） System, method and equipment for predicting risk of chronic obstructive pneumonia based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113409306A (en) * 2021-07-15 2021-09-17 推想医疗科技股份有限公司 Detection device, training method, training device, equipment and medium
CN114511562A (en) * 2022-04-19 2022-05-17 深圳市疾病预防控制中心（深圳市卫生检验中心、深圳市预防医学研究所） System, method and equipment for predicting risk of chronic obstructive pneumonia based on big data

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