WO2024128818A1 - Système de quantification de maladie pulmonaire d'un échantillon pulmonaire à l'aide d'une vision artificielle et d'un apprentissage automatique, et procédé de quantification de maladie pulmonaire à l'aide du système - Google Patents

Système de quantification de maladie pulmonaire d'un échantillon pulmonaire à l'aide d'une vision artificielle et d'un apprentissage automatique, et procédé de quantification de maladie pulmonaire à l'aide du système Download PDF

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WO2024128818A1
WO2024128818A1 PCT/KR2023/020600 KR2023020600W WO2024128818A1 WO 2024128818 A1 WO2024128818 A1 WO 2024128818A1 KR 2023020600 W KR2023020600 W KR 2023020600W WO 2024128818 A1 WO2024128818 A1 WO 2024128818A1
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emphysema
lung
unit
area
image
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Korean (ko)
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김진현
송대현
성주현
김아라
백승주
권민욱
김지연
김태규
최현주
최우식
이승환
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경상국립대학교산학협력단
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Priority claimed from KR1020220174910A external-priority patent/KR20240092297A/ko
Priority claimed from KR1020220174559A external-priority patent/KR20240091538A/ko
Application filed by 경상국립대학교산학협력단 filed Critical 경상국립대학교산학협력단
Publication of WO2024128818A1 publication Critical patent/WO2024128818A1/fr

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  • the present invention relates to a system for quantifying lung disease in lung samples and a method for quantifying lung disease using the same. It is configured to detect lung disease lesions in images of lung samples and use them to confirm the pneumonia rate and emphysema rate, based on expert opinion.
  • This relates to a lung disease quantification system for lung samples using computer vision and machine learning capable of standard quantification and a lung disease quantification method using the same.
  • Fine dust and ultrafine dust are known to have different components depending on the circumstances in which they are created and the surrounding environment, and the components of fine dust may include small particles, liquids, organic compounds, metals, and soil.
  • the concentration of heavy metals, known as hazardous substances, in the air around industrial complexes is high, so the impact of these heavy metals on health is likely to be greater.
  • the Ministry of Environment classifies those with an aerodynamic diameter of less than 10 ⁇ m as ultrafine dust 10 (PM10), and those with an aerodynamic diameter of less than 2.5 ⁇ m as ultrafine dust 2.5 (PM 2.5) (Ministry of Environment, Framework Act on Environmental Policy).
  • the primary route through which fine dust enters the body is the respiratory tract, and first, respiratory epithelial cells and alveolar macrophages are induced to produce inflammatory cytokines to remove foreign substances.
  • an inflammatory response occurs due to cytokines in the lung tissue, immune cells called Tcells act, reactive oxygen species (ROS) increase, and oxidative stress is induced by reactive oxygen species in the lung tissue. Oxidative stress again causes inflammation in lung tissue cells and DNA damage.
  • ROS reactive oxygen species
  • lung diseases caused by fine heavy metals are diagnosed by experts using lung photographs using X-ray or CT.
  • the lung disease includes pneumonia and emphysema.
  • Lung disease is diagnosed by calculating the area of diseased tissue compared to normal tissue. In this process, the expert's diagnosis has the disadvantage that it relies on each expert's experience and intuition, so there is significant inter-expert variability, and visual evaluation also takes a lot of time.
  • the present invention used machine learning to solve these shortcomings, which can provide more stable predictions at a much lower cost and thus can assist clinical pathologists much more easily when calculating ratios.
  • the present invention was created to solve the above problems, and the purpose of the present invention is to propose a method for quantifying lung disease in lung samples according to absolute standards using machine learning.
  • the purpose of the present invention is to provide a system that can modify quantification decisions based on human experience and intuition based on absolute quantification of lung disease in lung specimens.
  • An image acquisition unit (10) that biopsies the lung and acquires a lung image through microscopic imaging
  • a lung interstitium extraction unit 30 that extracts a lung interstitium area from the lung image from which the bronchioles have been removed;
  • a segmented image generator 40 that divides the lung image from which the lung interstitium area is extracted and generates a segmented image
  • a cell nucleus recognition unit 50 that recognizes a cell nucleus in the segmented image
  • a cell nucleus counting unit 60 that counts the number of recognized cell nuclei
  • a distribution map generator 70 that visualizes lung images according to the counted number of cell nuclei and generates a cell nucleus distribution map
  • a threshold comparison unit 80 that compares the counted number of cell nuclei with a threshold value
  • a pneumonia determination unit 90 that determines pneumonia when the number of cell nuclei counted is high compared to the threshold
  • an area calculation unit 100 that calculates a lung interstitium area in the segmented image determined to be pneumonia
  • An image acquisition step in which the image acquisition unit 10 acquires a lung image through microscopic imaging of a lung biopsy;
  • a bronchiole removal step in which the bronchiole removal unit 20 recognizes a bronchiole in the lung image and then removes the bronchiole;
  • a pulmonary interstitium extraction step in which the pulmonary interstitium extraction unit 30 extracts a pulmonary interstitium area from the lung image from which the bronchioles have been removed;
  • a segmented image generation step in which the segmented image generator 40 divides the lung image from which the lung interstitium area is extracted and then generates a segmented image;
  • the pneumonia rate calculation unit 110 is characterized in that it includes a pneumonia rate calculation step in which the pneumonia rate of the lung is calculated.
  • An image acquisition unit (10) that biopsies the lung and acquires a lung image through microscopic imaging
  • a binarization processing unit 30 that binarizes the lung image from which the bronchioles have been removed;
  • An air layer calculation unit 40 that calculates the area of the entire air layer in the binarized lung image
  • an alveolar removal unit 50 that removes the air layer of alveoli and extracts emphysema from the binarized lung image
  • a coordinate confirmation unit 60 that checks the extracted coordinates of emphysema
  • An emphysema detection unit 70 that detects emphysema whose coordinates have been confirmed;
  • An emphysema calculator 80 that extracts the detected emphysema and calculates the area
  • It is characterized in that it includes an emphysema quantification unit 90 that quantifies the emphysema rate of the extracted emphysema.
  • An image acquisition step in which the image acquisition unit 10 acquires a lung image through microscopic imaging of a lung biopsy;
  • a bronchiole removal step in which the bronchiole removal unit 20 recognizes a bronchiole in the lung image and then removes the bronchiole;
  • An air layer calculation step in which the air layer calculation unit 40 calculates the area of the entire air layer in the binarized lung image
  • An alveolar removal step in which the alveolar removal unit 50 removes the air layer of the alveoli and extracts emphysema from the binarized lung image;
  • It is characterized in that it includes an emphysema quantification step in which the emphysema quantification unit 90 quantifies the emphysema rate of the extracted emphysema.
  • the present invention can present a method for quantifying lung disease in lung samples according to absolute standards using machine learning.
  • the present invention provides a system that can modify quantification decisions based on human experience and intuition based on absolute quantification of lung disease in lung specimens.
  • Figure 1 is a schematic diagram of the pneumonia quantification system of the present invention.
  • Figure 2 is a flowchart of the present invention's pneumonia quantification method.
  • Figure 3 is a diagram showing a biopsy method for obtaining a lung specimen in the image acquisition step (S10) according to an embodiment of the present invention.
  • Figure 4 is a diagram confirming the disease characteristics in Figure 3 according to an embodiment of the present invention.
  • Figure 5 is a split image generated in the split image generation step (S40) according to an embodiment of the present invention.
  • Figure 6 is an image showing the cell nucleus recognition unit 50 and the cell nucleus counting unit 60 performing the cell nucleus recognition step (S50) and the cell nucleus counting step (S60) according to an embodiment of the present invention.
  • Figure 7 is an image of the pneumonia determination unit 90 performing the pneumonia determination step (S90) according to an embodiment of the present invention.
  • Figure 8 is a graph showing the distribution of the number of nuclei per unit area according to an embodiment of the present invention, (a) sample a, which the doctor determines to be 30%, and (b) sample b, which the doctor determines to be 90%.
  • Figure 9 relates to the distribution of the proportion of pneumonia in the samples of experts 1 and 2 according to an embodiment of the present invention, (a) when a patch containing 40 or more nuclei in a unit area is judged to be pneumonia, (b) unit (c) A patch containing more than 50 nuclei per unit area was judged to be pneumonia, and (c) a patch containing more than 60 nuclei per unit area was judged to be pneumonia.
  • Figure 10 is a quantified value of the pneumonia incidence rate based on the results quantified for each sample in Figure 9 according to an embodiment of the present invention.
  • Figure 11 shows the deviation between the pneumonia incidence rate quantification results performed through the present invention and the quantification results performed through Expert 1 (a) and Expert 2 (b) according to an embodiment of the present invention.
  • Figure 12 shows the results of pneumonia rates quantified by expert 1 according to an embodiment of the present invention.
  • Figure 13 shows the results of pneumonia rates quantified by expert 2 according to an embodiment of the present invention.
  • Figure 14 shows the quantitative results of pneumonia incidence (more than 50 unit area) performed through the present invention according to an embodiment of the present invention and the deviation of the quantitative results performed through Expert 1 (a) and Expert 2 (b).
  • Figure 15 is a configuration diagram of the emphysema quantification system of the present invention.
  • Figure 16 is a more detailed configuration diagram of the emphysema quantification system of the present invention.
  • Figure 17 is a flowchart of the emphysema quantification method of the present invention.
  • FIG. 18 is a more detailed flowchart of the emphysema quantification method of the present invention.
  • Figure 19 is a diagram showing a biopsy method for obtaining a lung specimen in the image acquisition step (S10) according to an embodiment of the present invention.
  • Figure 20 is a diagram confirming the disease characteristics in Figure 17 according to an embodiment of the present invention.
  • Figure 21 is a diagram showing the binarization processing unit 30 step-by-step binarization processing of the lung image from which the bronchioles have been removed in the third step (S30) binarization processing step according to an embodiment of the present invention. .
  • Figure 22 is a graph showing the comparison of results with expert 1 for the emphysema analogy when steps 12 (a) to 15 (d) among the 15 steps (Steps) of Figure 21 in experimental verification according to an embodiment of the present invention. .
  • Figure 23 is a graph showing the comparison of results with expert 2 for the emphysema analogy when steps 12 (a) to 15 (d) among the 15 steps (Steps) of Figure 21 in experimental verification according to an embodiment of the present invention.
  • Figure 24 shows the deviation of the quantification results performed by Expert 1 and Expert 2 at steps 12 to 15 among the 15 steps of Figure 21 according to an embodiment of the present invention. It is shown.
  • Figure 25 shows the results of emphysema incidence rate quantification performed through the present invention at step 12 to step 15 among the 15 steps (Steps) of Figure 21 according to an embodiment of the present invention, and expert 1 (a) and expert 2 (b) This shows the deviation of the quantification results performed through .
  • Figure 26 is a mechanical measurement result of emphysema quantification by expert 1 when steps 12 to 15 of the 15 steps (Steps) of Figure 21 are performed according to an embodiment of the present invention.
  • Figure 27 is a mechanical measurement result of emphysema quantification by expert 2 at steps 12 to 15 among the 15 steps (Steps) of Figure 21 according to an embodiment of the present invention.
  • Pneumonia and emphysema can be evaluated qualitatively and quantitatively through chest X-ray or CT.
  • the method according to the present invention reflected qualitative judgment based on the expert's experience and intuition, and applied the same quantitative method to all samples to absolutely quantify pneumonia and emphysema.
  • the present invention determined qualitative standards based on expert judgment to quantify pneumonia and emphysema. In addition, in order to verify the accuracy of the quantified method according to qualitative standards, verification was conducted according to the judgment of two experts.
  • pneumonia was determined by the number of nuclei in the cells and quantified according to the density of the nuclei.
  • the density quantified most similar to the expert's judgment is when the number of nuclei per unit area is 50 or more. Therefore, based on this, quantification standards were set and expert judgment and comparative analysis were conducted.
  • the two experts judged the pneumonia rate to be an intermediate rate (45 to 70) the difference between the two experts was small and stable. Meanwhile, quantification at a low ratio (0 to 45) showed that cases with high nuclear density were judged to be pneumonia, while quantification at high ratios (70 to 90) showed that cases with low nuclear density were judged to be pneumonia.
  • emphysema was quantified by removing normal alveoli according to the erode stage and detecting only the emphysema area.
  • the quantification criteria were determined based on the qualitative judgment of experts regarding the results we detected. As a result, the deviation from expert 1's quantification results was lowest at erosion steps 14 and 12 in the normal alveolar removal stage.
  • the results quantified by two experts at a low ratio (0 to 10) showed the smallest deviation from the quantified results. In step 15, the higher the ratio, the smaller the deviation from the results quantified in step 12. This showed that the two experts judged that even a small air gap was emphysema because the degree of emphysema in the sample was highly quantified.
  • the higher the degree of emphysema the greater the deviation from our results, showing that experts performed unstable quantification at a high rate.
  • the quantification method proposed in the present invention can analyze the quantification judgment of experts.
  • quantifying pneumonia and emphysema it was possible to identify experts' judgment trends and differences between experts. And although experts said they quantified it according to the absolute standards of pathology, it appeared to reflect the experts' visual experience and intuition.
  • This invention proposed a method for absolute quantification of pneumonia and emphysema in lung samples using machine learning and classical computer vision techniques. They showed that quantifying pneumonia and emphysema for each sample based on methods developed by experts can lead to different results. It also showed that the expert's qualitative judgment could vary depending on the degree of pneumonia and emphysema in the sample. Therefore, our quantification method can be a standard for quantification and can be used as a tool to correct the quantitative evaluation results of experts when they quantitatively evaluate the results.
  • the pneumonia quantification system for lung specimens using computer vision and machine learning includes an image acquisition unit (10), a bronchioles removal unit (20), a lung interstitial extraction unit (30), and a segmented image.
  • the image acquisition unit 10 biopsies the lung and acquires an image of the lung through microscopic imaging.
  • the image acquisition unit 10 acquires lung biopsy samples exposed to various heavy metals such as nickel, chromium manganese, and cadmium.
  • One lung image represents one slide, and one slide consists of 2 to 4 sections.
  • the bronchiole removal unit 20 recognizes the bronchiole in the lung image and removes the bronchiole.
  • the bronchiole removal unit 20 must label the bronchioles in order to recognize them in the lung image and learn to remove the bronchiole.
  • the bronchiole removal unit 20 divides the lung image into predetermined sizes to set partitions, and labels some of the set partitions as bronchioles areas. Among the set partitions, some of the partitions that are not labeled as the bronchial region are used as learning data for learning, and the rest are used as test data for testing.
  • the bronchiole removal unit 20 regathers the partitions to be divided after labeling of the bronchiole region is completed and regenerates one lung image.
  • a 5x5 partition is created and the bronchioles are labeled directly within this partition.
  • One partition is 1,640 wide and 1,440 long.
  • the total number of partitions is 5,925, and labeling is performed on 50, 40 are used as train data, and the remaining 10 are used as test data. After learning, the bronchiolar regions within all sections are segmented and classified, and the partition images are collected to form a single image.
  • the lung interstitium extraction unit 30 extracts the lung interstitium area from the lung image from which the bronchiole has been removed.
  • the lung interstitium extraction unit 30 labels the lung interstitium area with pixels of the same color and then extracts the labeled pixel area.
  • the connected pulmonary interstitial area is labeled with pixels of the same color using connected components of OpenCV, one of the computer vision technologies, and the pixel area of this label is extracted.
  • the segmented image generator 40 divides the lung image from which the lung interstitium area is extracted and generates a segmented image.
  • the split image generator 40 prepares the split images to have the same width and length.
  • the sample is divided to facilitate visual identification of the nucleus.
  • image_slicer the sample is divided into 82 and 72 partitions, respectively, with size 100x100.
  • the cell nucleus recognition unit 50 recognizes the cell nucleus in the segmented image.
  • the cell nucleus can be recognized using YOLOv5.
  • YOLOv5 learning black nuclei are captured in the partition and the labeled data are all made of the same class.
  • one section is divided into several partitions to detect nuclei.
  • One partition is divided into 82 and 72 partitions of equal width and length, with a width of 100 pixels and a length of 100 pixels.
  • the total number of partitions is 1,399,248, of which 100 partitions are labeled, 90 training data and 10 test data are used to train YOLOv5.
  • the cell nucleus counting unit 60 counts the number of recognized cell nuclei.
  • the cell nucleus counting unit 60 counts the number of cell nuclei recognized in the segmented image through the learning.
  • the distribution map generator 70 generates a cell nucleus distribution map by visualizing the lung image according to the counted number of cell nuclei.
  • the distribution map generator 70 selects all segmented images suspected of having pneumonia and generates a distribution map of the cell nuclei based on the segmented images, as shown in FIG. 8.
  • the threshold comparison unit 80 compares the counted number of cell nuclei and the threshold.
  • the method used by experts calculates the overall ratio by calculating the number of cell nuclei per unit area.
  • unit area was used as the size of each segmented image. The important point here is that the number of cell nuclei per unit area is the standard for determining the presence or absence of pneumonia. However, there is no standard for cell density per unit area that traditionally determines pneumonia. Therefore, in the present invention, the number of cell nuclei per unit area that determines the most appropriate pneumonia was determined by applying various densities to the unit area used and comparing them with expert judgment.
  • the pneumonia determination unit 90 determines pneumonia when the number of cell nuclei counted is high compared to the threshold.
  • Figure 8 shows an example of the distribution of the number of nuclei per unit area.
  • This distribution diagram follows a Gaussian distribution, and this distribution diagram alone cannot specify the number of nuclei per unit area that identifies an outlier, that is, pneumonia. Therefore, the number of nuclei present in each partition was analyzed according to the proportion of pneumonia quantified by experts. For example, if an expert says that pneumonia is 30%, the minimum number of nuclei per unit area that accounts for the top 30% in the distribution chart above was found, and the number of nuclei per unit area that specifies pneumonia was found.
  • the area calculation unit 100 calculates the lung interstitium area in the segmented image determined to be pneumonia.
  • the area calculation unit 100 calculates the lung interstitium area in the segmented image as a dense nucleus on a pixel basis.
  • the pneumonia rate calculation unit 110 calculates the pneumonia rate of the lung.
  • the pneumonia rate calculation unit 110 calculates the pneumonia rate according to [Equation 1] below.
  • a P is the area of the pulmonary interstitial area calculated by the area calculation unit 100
  • a INT is the area of the pulmonary interstitial area extracted by the pulmonary interstitial extraction unit 30.
  • the method for quantifying pneumonia in lung specimens using computer vision and machine learning according to the present invention quantifies pneumonia using the pneumonia quantification system (1).
  • the first step (S10) is the image acquisition step.
  • the image acquisition unit 10 biopsies the lung and acquires a lung image through microscopic imaging. As shown in FIG. 3, the image acquisition unit 10 acquires lung biopsy samples exposed to various heavy metals such as nickel, chromium manganese, and cadmium. One lung image represents one slide, and one slide consists of 2 to 4 sections.
  • the second step (S20) is the bronchioles removal step.
  • the bronchioles removal unit 20 recognizes the bronchioles in the lung image and then removes the bronchioles. More specifically, the bronchiole removal unit 20 must label the bronchioles in order to recognize them in the lung image and learn to remove the bronchiole.
  • the bronchiole removal unit 20 divides the lung image into predetermined sizes to set partitions, and labels some of the set partitions as bronchioles areas. Among the set partitions, some of the partitions that are not labeled as the bronchial region are used as learning data for learning, and the rest are used as test data for testing.
  • the bronchiole removal unit 20 regathers the partitions to be divided after labeling of the bronchiole region is completed and regenerates one lung image.
  • a 5x5 partition is created and the bronchioles are labeled directly within this partition.
  • One partition is 1,640 wide and 1,440 long.
  • the total number of partitions is 5,925, and labeling is performed on 50, 40 are used as training data, and the remaining 10 are used as test data.
  • the bronchiolar regions within all sections are segmented and classified, and the partition images are collected to form a single image.
  • the third step (S30) is the pulmonary interstitium extraction step.
  • the lung interstitium extraction unit 30 extracts the lung interstitium area from the lung image from which the bronchiole has been removed. More specifically, the lung interstitium extraction unit 30 labels the lung interstitium area with pixels of the same color and then extracts the labeled pixel area.
  • the connected pulmonary interstitial area is labeled with pixels of the same color using connected components of OpenCV, one of the computer vision technologies, and the pixel area of this label is extracted.
  • the fourth step (S40) is a segmented image generation step.
  • the segmented image generator 40 divides the lung image from which the lung interstitium area is extracted and generates a segmented image. More specifically, the split image generator 40 prepares the split images to have the same width and length.
  • the sample is divided to facilitate visual identification of the nucleus.
  • image_slicer the sample is divided into 82 and 72 partitions, respectively, with size 100x100.
  • the fifth step (S50) is the cell nucleus recognition step.
  • the cell nucleus recognition unit 50 recognizes the cell nucleus in the segmented image.
  • the cell nucleus can be recognized using YOLOv5.
  • YOLOv5 learning black nuclei are captured in the partition and the labeled data are all made of the same class.
  • To detect nuclei in high-quality specimen images one section is divided into several partitions to detect nuclei.
  • One partition is divided into 82 and 72 partitions of equal width and length, with a width of 100 pixels and a length of 100 pixels. The total number of partitions is 1,399,248, 100 partitions are labeled, 90 training data and 10 test data are used to train YOLOv5.
  • the sixth step (S60) is the cell nucleus counting step.
  • the cell nucleus counting unit 60 counts the number of recognized cell nuclei.
  • the seventh step (S70) is the distribution map generation step.
  • the distribution map generating unit 70 In the seventh step (S70), the distribution map generating unit 70 generates a cell nucleus distribution map by visualizing the lung image according to the counted number of cell nuclei. More specifically, the distribution map generator 70 selects all segmented images suspected of having pneumonia and generates a distribution map of the cell nuclei based on the segmented images, as shown in FIG. 8.
  • the eighth step (S80) is a threshold comparison step.
  • the threshold value comparison unit 80 compares the counted number of cell nuclei and the threshold value.
  • the method used by experts calculates the overall ratio by calculating the number of cell nuclei per unit area.
  • unit area was used as the size of each segmented image. The important point here is that the number of cell nuclei per unit area is the standard for determining the presence or absence of pneumonia. However, there is no standard for cell density per unit area that traditionally determines pneumonia. Therefore, in the present invention, the number of cell nuclei per unit area that determines the most appropriate pneumonia was determined by applying various densities to the unit area used and comparing them with expert judgment.
  • the ninth step (S90) is the pneumonia determination step.
  • the pneumonia determination unit 90 determines pneumonia when the number of cell nuclei counted is greater than the threshold value.
  • Figure 8 shows an example of the distribution of the number of nuclei per unit area.
  • This distribution diagram follows a Gaussian distribution, and this distribution diagram alone cannot specify the number of nuclei per unit area that identifies an outlier, that is, pneumonia. Therefore, the number of nuclei present in each partition was analyzed according to the proportion of pneumonia quantified by experts. For example, if an expert says that pneumonia is 30%, the minimum number of nuclei per unit area that accounts for the top 30% in the distribution chart above was found, and the number of nuclei per unit area that specifies pneumonia was found.
  • the tenth step (S10) is the area calculation step.
  • the area calculation unit 100 calculates the lung interstitium area in the segmented image determined to be pneumonia. More specifically, the area calculation unit 100 calculates the lung interstitium area in the segmented image as a dense nucleus on a pixel basis.
  • the 11th step (S110) is the pneumonia rate calculation step.
  • the pneumonia rate calculation unit 110 calculates the pneumonia rate of the lung. More specifically, the pneumonia rate calculation unit 110 calculates the pneumonia rate according to [Equation 1] below.
  • a P is the area of the pulmonary interstitial area calculated by the area calculation unit 100
  • a INT is the area of the pulmonary interstitial area extracted by the pulmonary interstitial extraction unit 30.
  • mice Seven-week-old C57BL/6 mice were selected as experimental animals and exposed to mouse lung tissue samples and diluted nickel, chromium, manganese, and cadmium at concentrations of 50nM, 20nM, 10nM, and 10nM, respectively. A total of 70 samples were collected through the nasal cavity, alone or in combination, once a day for 4 weeks. This experiment was conducted at the University of Ulsan SPF (no specific pathogens added) and was reviewed by the Animal Invention Center of the University of Ulsan (IACUC) (IACUC No. BSK-21-030).
  • IACUC Animal Invention Center of the University of Ulsan
  • the 70 specimens collected consisted of each slide.
  • Figure 3 shows one slide, and one slide consists of 2-4 sections. Therefore, a total of 70 slides were sampled and cross-sectional data were collected from 237 slides. Of these, only 70 sections used by actual doctors were used.
  • One section is a high-quality image measuring 8200 wide and 7200 long.
  • the rate of pneumonia was quantified, and the method for confirming the rate of pneumonia through a sample is as follows.
  • the ratio is quantified by calculating the area of the interstitial area where many nuclei are concentrated compared to the total interstitial area of the specimen excluding the air passage layer.
  • the index for calculating the rate of pneumonia is as follows [Equation 1].
  • a P is the area of the pulmonary interstitial area calculated by the area calculation unit 100
  • a INT is the area of the pulmonary interstitial area extracted by the pulmonary interstitial extraction unit 30.
  • YOLACT learning requires bronchioles labels. Because the size of the bronchioles is relatively small compared to the overall section, we divide them into 5x5 partitions and label the bronchioles directly within these partitions. One partition is 1,640 wide and 1,440 long. The total number of partitions is 5,925, and labeling is performed on 50, 40 are used as train data, and the remaining 10 are used as test data.
  • the bronchial regions within every section are segmented and classified.
  • YOLOv5 learning black nuclei are captured in the segmented image, and all label data are made of the same class.
  • One section is divided into several divisions to detect nuclei.
  • One segmented image is divided into 82 horizontal and 72 partitions of equal width and length, with a width of 100 pixels and a length of 100 pixels.
  • the total number of segmented images was 1,399,248, of which 100 segmented images were labeled, with 90 training data and 10 test data.
  • YOLOv5 learning is performed to determine the number of detected nuclei for each segmented image.
  • the method used by experts is to calculate the overall ratio by counting the number of cell nuclei per unit area.
  • the unit area is also used as the size of each divided image.
  • the important point here is that the number of cell nuclei per unit area is the standard for determining the presence or absence of pneumonia. However, there is no standard for cell density per unit area that traditionally determines pneumonia. Therefore, in the present invention, the number of cell nuclei per unit area that determines the most appropriate pneumonia was determined by applying various densities to the unit area used and comparing them with expert judgment.
  • Figure 8 shows an example of the distribution of the number of nuclei per unit area.
  • This distribution chart follows a Gaussian distribution, and this distribution chart alone cannot specify the number of nuclei per unit area that identifies an outlier, that is, pneumonia. Therefore, the number of nuclei present in each segmented image was analyzed according to the proportion of pneumonia quantified by experts. For example, if an expert says that pneumonia is 30%, the minimum number of nuclei per unit area that accounts for the top 30% in the distribution chart above was found, and the number of nuclei per unit area that specifies pneumonia was found.
  • the area of the lung interstitium area calculated by the area calculation unit 100 and the area of the lung interstitium area extracted by the lung interstitium extraction unit 30 are calculated as dense nuclei in pixel units.
  • the final pneumonia rate is quantified by calculating the area of the lung interstitium where the nuclei are concentrated compared to the total lung interstitium area.
  • Figure 9 shows the results of experts 1 and 2 quantifying the pneumonia rate according to the quantification of each sample and the number of nuclei in each segmented image.
  • the x-axis represents the number of samples and the y-axis represents the rate of pneumonia for each sample.
  • Figure 9(a) shows that most of our results are calculated higher than those quantified by experts, meaning that experts consider pneumonia when the criterion for quantifying pneumonia is concentrated in more than 40 nuclei per unit area. .
  • Figure 9(b) can be judged to be more similar to that of the expert than Figure 9(a).
  • the error was the smallest among the 60 ranges quantified by experts.
  • our quantification results were smaller than those of experts. This means that even a concentration of less than 50 nuclei per unit area, when quantified by experts at a high rate, is considered pneumonia.
  • Figure 10 confirms that the rate of pneumonia quantified had the smallest deviation between the two experts when there were more than 50 nuclei per unit area. This can be confirmed through Figure 9(b).
  • the results of quantifying the rate of pneumonia when there were more than 50 nuclei per unit area were most similar to the quantitative inventions of experts, and our quantification method was absolute because it quantified the rate of pneumonia by identifying the number of nuclei present in the sample. . Therefore, the rate of pneumonia can be quantified using absolute standards based on qualitative analysis based on the experience and intuition of experts. Below, we compare and analyze the quantification results of experts based on quantification based on more than 50 nuclei per unit area.
  • the quantification method provided by the present invention provides observations and considerations of traditional quantification methods for human and mouse samples.
  • the sample data of the present invention was collected for the purpose of pathology invention together with Changwon Gyeongsang National University Hospital and consists of a total of 237 lung sample images.
  • To evaluate the accuracy of the proposed method for quantifying pneumonia and emphysema we collected data on the results of the invention for quantifying pneumonia and emphysema as judged by two independent pathologists.
  • data judged by a pathology expert were collected, and all were judged by a pathology expert at Changwon Gyeongsang National University Hospital.
  • the image size of each neural network model used in the present invention is YOLOv5 640x640, YOLACT is 1,640x1, 440 pixel size (neural network input size), and RGB 32-bit image.
  • the equipment used in the YOLOv5 and YOLACT experiments was Ubuntu 20.04 LTS and the GPU was GeForce RTX 3090 Ti GPU, and the OS was implemented in python. And the equipment used in the experiment using computer vision was implemented in python using Windows 10 as the OS and Geforce RTX 2080 Ti GPU as the GPU.
  • Figure 11 shows the difference between the pneumonia results quantified by the expert and the results of the expert's qualitative analysis of the sample. Deviation was calculated using the sliding window method based on the proportion of pneumonia quantified by experts.
  • Figure 11 shows the results of a comparative analysis of the pneumonia rate quantified by experts based on the pneumonia rate quantified by us.
  • the degree of pneumonia quantified by the two experts was closest to the machine's judgment, in the mid-range of 45 to 70. This means that the two experts quantified the severity of pneumonia more stably in the mid-range (45-70) than in the relatively low range (0-45) and high range (70-90).
  • the deviation from the quantitative result was the smallest when it was 60 or more in the range from 0 to 20. In the range of 20 to 60, the deviation from the quantitative result was smallest when it was 50 or more. And in the range of 60 or more, the deviation from the quantified results was the smallest when there were 40 or more.
  • Figures 12 and 13 also show that experts tend to judge pneumonia as having a low nuclear concentration per unit area within the high range.
  • Figure 12 shows the quantified results according to the pneumonia rate quantified by expert 1, and most similar results to the quantified results were shown when the nuclear density distribution was 40 or more. Expert 1 even considered that most cases of pneumonia were less than 40 per unit area.
  • Figure 30 also shows that in the range where the pneumonia rate quantified by expert 2 is high, cases where the nuclear density per unit area is less than 40 are considered pneumonia.
  • Figure 14 shows the deviation of expert results based on the quantification results according to the present invention (proportion of pneumonia quantified when there are 50 or more per unit area).
  • the x-axis represents the rate of pneumonia and the y-axis represents the deviation.
  • Figure 14(a) shows the deviation from expert 1 based on the results of the present invention, and the expert quantified pneumonia at a higher level than the results of the present invention in the pneumonia degree range of 50 or higher.
  • Figure 14(b) shows the deviation from expert 2 based on the results of the present invention, and the expert quantified pneumonia at a higher level than the results of the present invention in the pneumonia degree range of 50 or higher.
  • a bronchiole removal step in which the bronchiole removal unit 20 recognizes a bronchiole in the lung image and then removes the bronchiole.
  • a pulmonary interstitium extraction step in which the pulmonary interstitium extraction unit 30 extracts the pulmonary interstitium area from the lung image from which the bronchioles have been removed.
  • a segmented image generation step in which the segmented image generator 40 divides the lung image from which the lung interstitium area is extracted and then generates a segmented image.
  • S50 A cell nucleus recognition step in which the cell nucleus recognition unit 50 recognizes the cell nucleus in the segmented image.
  • S80 A threshold comparison step in which the threshold comparison unit 80 compares the counted number of cell nuclei with the threshold value.
  • the emphysema quantification system for lung specimens using computer vision and machine learning includes an image acquisition unit (10), a bronchioles removal unit (20), a binarization processing unit (30), and an air layer. It is characterized by comprising a calculation unit 40, an alveolar removal unit 50, a coordinate confirmation unit 60, an emphysema detection unit 70, an emphysema calculation unit 80, and an emphysema quantification unit 90.
  • the image acquisition unit 10 biopsies the lung and acquires an image of the lung through microscopic imaging.
  • the image acquisition unit 10 acquires lung biopsy samples exposed to various heavy metals such as nickel, chromium manganese, and cadmium.
  • One lung image represents one slide, and one slide consists of 2 to 4 sections.
  • the bronchiole removal unit 20 recognizes the bronchiole in the lung image and removes the bronchiole.
  • the bronchiole removal unit 20 must label the bronchioles in order to recognize them in the lung image and learn to remove the bronchiole.
  • the bronchiole removal unit 20 divides the lung image into predetermined sizes to set partitions, and labels some of the set partitions as bronchioles areas. Among the set partitions, some of the partitions that are not labeled as the bronchial region are used as learning data for learning, and the rest are used as test data for testing.
  • the bronchiole removal unit 20 regathers the partitions to be divided after labeling of the bronchiole region is completed and regenerates one lung image.
  • a 5x5 partition is created and the bronchioles are labeled directly within this partition.
  • One partition is 1,640 wide and 1,440 long.
  • the total number of partitions is 5,925, and labeling is performed on 50, 40 are used as train data, and the remaining 10 are used as test data. After learning, the bronchiolar regions within all sections are segmented and classified, and the partition images are collected to form a single image.
  • the binarization processing unit 30 binarizes the lung image from which the bronchioles have been removed.
  • the binarization processing unit 30 processes the pulmonary interstitium and bronchiole areas with the first color (1), and processes the alveoli and emphysema areas with the second color (0).
  • the binarized lung image that has passed through the binarization processing unit 30 is processed step by step as shown in FIG. 21 to search for emphysema, removing general alveoli and leaving emphysema.
  • the stages reflected the opinion that steps 13 to 15 were most appropriate through consultation with pathology experts.
  • the interstitial and bronchial regions are colored black using the OpenCV library. Additionally, alveoli and emphysema are treated in white.
  • the air layer calculation unit 40 calculates the total air layer area in the binarized lung image.
  • the air layer calculation unit 40 calculates the sum of the pixel area of the label designation unit 41 that specifies a label in the binarized lung image and the designated label. It consists of an air layer pixel extraction unit 42 that extracts the entire air layer pixel area.
  • OpenCV's connectedComponent is used to label air layers in a sample. Afterwards, the total air layer pixel area is extracted by calculating the sum of the pixel areas of the specified labels.
  • the alveolar removal unit 50 removes the air layer of alveoli from the binarized lung image and extracts emphysema.
  • Normal alveoli are smaller and relatively round in shape than those of emphysema or lung bronchioles. Therefore, if the pulmonary interstitial layer becomes thicker, the alveoli disappear due to the thickened interstitial layer, and only emphysema or bronchioles remain. In other words, because the bronchioles were previously removed, if the pulmonary interstitial layer is thickened, only the emphysema has an air layer, and this part is judged to be emphysema.
  • OpenCV's binarization process is used to perform a step-by-step erosion process for the air layer region.
  • the step-by-step erode process the normal alveoli are removed, leaving only the features of emphysema.
  • the coordinate confirmation unit 60 confirms the extracted coordinates of emphysema.
  • the coordinate confirmation unit 60 includes an emphysema designation unit 61 that specifies a label for emphysema extracted from the binarized lung image, and a center coordinate in the designated emphysema label. It consists of a central coordinate extraction unit 62 that extracts.
  • the connected component of OpenCV is used to label the remaining emphysema features of the sample. Afterwards, the center coordinate is extracted from the specified label.
  • the emphysema detection unit 70 detects emphysema whose coordinates have been confirmed.
  • the emphysema detection unit 70 is an image mapping unit ( 71) and an emphysema color designation unit 72 that designates a color to the emphysema area based on the coordinates of the center of the emphysema.
  • the image mapping unit 71 maps the center coordinates of emphysema detected by the coordinate confirmation unit 60 to the original lung image to calculate the actual size of emphysema in the emphysema calculation unit 80 below.
  • the emphysema color designation unit 72 colors only the identified emphysema to be distinguished.
  • the center coordinates of the confirmed emphysema are mapped to the original image of the image acquisition unit 10 to the original lung image. Afterwards, a color is assigned to the emphysema area using the BFS (Breadth First Search) algorithm based on the center coordinates.
  • BFS Bitth First Search
  • the emphysema calculator 80 extracts the detected emphysema and calculates the area.
  • the emphysema calculation unit 80 checks the emphysema area detected by the emphysema detection unit 70, and then configures the emphysema area designation unit 81 to label the emphysema area and the labeled emphysema area. It consists of an emphysema pixel extraction unit 82 that extracts the area of emphysema pixels by calculating the sum of pixels in the area.
  • inRange is used to extract the area of emphysema detected by the BFS (Breadth First Search) algorithm.
  • BFS Bitth First Search
  • the extracted emphysema region is labeled using OpenCV's connectedComponents.
  • the area of the emphysema pixel is extracted by calculating the sum of the pixel areas of the designated labels.
  • Emphysema can be detected by substituting the center coordinates extracted in the previous step into the original image and coloring the emphysema area. Nodes are set to pixels and black areas are considered gaps. At this time, it was set to search the white area. The extraction coordinates are set to the first node and the search area is set to change from white to red pixels.
  • the emphysema quantification unit 90 quantifies the emphysema rate of the extracted emphysema.
  • the emphysema determination unit 90 calculates the emphysema rate according to [Equation 1] below.
  • Emphysema rate A a / A emp
  • a a is the area of the total air layer calculated by the air layer calculator 40
  • a emp is the sum of A a and the emphysema area calculated by the emphysema calculator 80).
  • the area of the total air layer extracted by the air layer calculator 40 and the detected emphysema area calculated by the emphysema calculator 80 are calculated on a pixel basis.
  • the final proportion of emphysema is quantified by calculating the area of emphysema relative to the area of the entire air space.
  • the method for quantifying emphysema in lung samples using computer vision and machine learning according to the present invention quantifies emphysema using the emphysema quantification system (1), as shown in FIGS. 17 and 18.
  • the first step (S10) is the image acquisition step.
  • the image acquisition unit 10 biopsies the lung and acquires a lung image taken with a microscope. As shown in FIG. 17, the image acquisition unit 10 acquires lung biopsy samples exposed to various heavy metals such as nickel, chromium manganese, and cadmium. One lung image represents one slide, and one slide consists of 2 to 4 sections.
  • the second step (S20) is the bronchioles removal step.
  • the bronchioles removal unit 20 recognizes the bronchioles in the lung image and then removes the bronchioles. More specifically, the bronchiole removal unit 20 must label the bronchioles in order to recognize them in the lung image and learn to remove the bronchiole.
  • the bronchiole removal unit 20 divides the lung image into predetermined sizes to set partitions, and labels some of the set partitions as bronchioles areas. Among the set partitions, some of the partitions that are not labeled as the bronchial region are used as learning data for learning, and the rest are used as test data for testing.
  • the bronchiole removal unit 20 regathers the partitions to be divided after labeling of the bronchiole region is completed and regenerates one lung image.
  • a 5x5 partition is created and the bronchioles are labeled directly within this partition.
  • One partition is 1,640 wide and 1,440 long.
  • the total number of partitions is 5,925, and labeling is performed on 50, 40 are used as train data, and the remaining 10 are used as test data. After learning, the bronchiolar regions within all sections are segmented and classified, and the partition images are collected to form a single image.
  • the third step (S30) is a binarization processing step.
  • the binarization processing unit 30 binarizes the lung image from which the bronchioles have been removed. More specifically, the binarization processing unit 30 processes the pulmonary interstitium and bronchiole areas with the first color (1), and processes the alveoli and emphysema areas with the second color (0).
  • the binarized lung image that has passed through the binarization processing unit 30 is processed step by step as shown in FIG. 21 to search for emphysema, removing general alveoli and leaving behind emphysema.
  • the stages reflected the opinion that steps 13 to 15 were most appropriate through consultation with pathology experts.
  • the interstitial and bronchial regions are colored black using the OpenCV library. Additionally, alveoli and emphysema are treated in white.
  • the fourth step (S40) is the air space calculation step.
  • the air layer calculation unit 40 calculates the total area of the air layer in the binarized lung image. More specifically, in the fourth step (S40), the air layer calculation unit 40 includes a label designation unit 41 that specifies a label in the binarized lung image and the designated label. It consists of an air layer pixel extraction unit 42 that extracts the entire air layer pixel area by calculating the sum of the pixel areas.
  • OpenCV's connectedComponent is used to label air layers in a sample. Afterwards, the total air layer pixel area is extracted by calculating the sum of the pixel areas of the specified labels.
  • the fifth step (S50) is the alveolar removal step.
  • the alveolar removal unit 50 removes the air layer of the alveoli from the binarized lung image and extracts emphysema.
  • Normal alveoli are smaller and relatively round in shape than those of emphysema or lung bronchioles. Therefore, if the pulmonary interstitial layer becomes thicker, the alveoli disappear due to the thickened interstitial layer, and only emphysema or bronchioles remain. In other words, because the bronchioles were previously removed, if the pulmonary interstitial layer is thickened, only the emphysema has an air layer, and this part is judged to be emphysema.
  • OpenCV's binarization process is used to perform a step-by-step erosion process for the air layer region.
  • the step-by-step erode process the normal alveoli are removed, leaving only the features of emphysema.
  • the sixth step (S60) is the coordinate confirmation step.
  • the coordinate confirmation unit 60 confirms the extracted coordinates of emphysema. More specifically, the sixth step (S60) is an emphysema designation step (S61) in which the emphysema designation unit 61 assigns a label to the emphysema extracted from the binarized lung image and central coordinate extraction. It consists of a center coordinate extraction step (S62) in which the unit 62 extracts the center coordinate from the designated emphysema label.
  • the connected component of OpenCV is used to label the remaining emphysema features of the sample. Afterwards, the center coordinate is extracted from the specified label.
  • the seventh step (S70) is the emphysema detection step.
  • the emphysema detection unit 70 detects emphysema whose coordinates are confirmed. More specifically, in the seventh step (S70), the image mapping unit 71 matches the center coordinates of emphysema confirmed by the coordinate confirmation unit 60 to the lung image acquired by the image acquisition unit 10 to the original lung image. It consists of an image mapping step (S71) and an emphysema color designation step (S72) in which the emphysema color designation unit 72 assigns a color to the emphysema area based on the center coordinates of the emphysema.
  • the image mapping unit 71 maps the center coordinates of emphysema detected by the coordinate confirmation unit 60 to the original lung image and actualizes them in the emphysema calculation unit 80 below.
  • the size of emphysema is calculated.
  • the emphysema color designation unit 72 colors only the identified emphysema to distinguish it.
  • the center coordinates of the confirmed emphysema are mapped to the original image of the image acquisition unit 10 to the original lung image. Afterwards, a color is assigned to the emphysema area using the BFS (Breadth First Search) algorithm based on the center coordinates.
  • BFS Bitth First Search
  • the eighth step (S80) is the emphysema calculation step.
  • the emphysema calculation unit 80 extracts the detected emphysema and calculates the area.
  • the eighth step (S80) is an emphysema area designation step in which the emphysema area designation unit 81 confirms the emphysema area detected by the emphysema detection unit 70 and then labels the emphysema area ( S81) and an emphysema pixel extraction step (S82) in which the emphysema pixel extraction unit 82 extracts the area of the emphysema pixel by calculating the sum of pixels of the labeled emphysema area.
  • inRange is used to extract the area of emphysema detected by the BFS (Breadth First Search) algorithm.
  • BFS Bitth First Search
  • the extracted emphysema region is labeled using OpenCV's connectedComponents.
  • the area of the emphysema pixel is extracted by calculating the sum of the pixel areas of the designated labels.
  • Emphysema can be detected by substituting the center coordinates extracted in the previous step into the original image and coloring the emphysema area. Nodes are set to pixels and black areas are considered gaps. At this time, it was set to search the white area. The extraction coordinates are set to the first node and the search area is set to change from white to red pixels.
  • the ninth step (S90) is the emphysema quantification step.
  • the emphysema quantification unit 90 quantifies the emphysema rate of the extracted emphysema.
  • the emphysema determination unit 90 calculates the emphysema rate according to [Equation 1] below.
  • Emphysema rate A a / A emp
  • a a is the area of the total air layer calculated by the air layer calculator 40
  • a emp is the sum of A a and the emphysema area calculated by the emphysema calculator 80).
  • the area of the total air layer extracted by the air layer calculator 40 and the detected emphysema area calculated by the emphysema calculator 80 are calculated on a pixel basis.
  • the final proportion of emphysema is quantified by calculating the area of emphysema relative to the area of the entire air space.
  • mice Seven-week-old C57BL/6 mice were selected as experimental animals and exposed to mouse lung tissue samples and diluted nickel, chromium, manganese, and cadmium at concentrations of 50nM, 20nM, 10nM, and 10nM, respectively. A total of 70 samples were collected through the nasal cavity, alone or in combination, once a day for 4 weeks. This experiment was conducted at the University of Ulsan SPF (no specific pathogens added) and was reviewed by the Animal Invention Center of the University of Ulsan (IACUC) (IACUC No. BSK-21-030).
  • IACUC Animal Invention Center of the University of Ulsan
  • the 70 specimens collected consisted of each slide.
  • Figure 17 shows one slide, and one slide consists of 2-4 sections. Therefore, a total of 70 slides were sampled and cross-sectional data were collected from 237 slides. Of these, only 70 sections actually used by doctors were used.
  • One section is a high-quality image measuring 8200 wide and 7200 long.
  • the rate of emphysema was quantified, and the method for confirming the rate of emphysema through a sample is as follows.
  • the proportion of emphysema is quantified by calculating the air space of emphysema compared to the total air space excluding bronchioles.
  • Emphysema rate A a / A emp
  • a a is the area of the total air layer calculated by the air layer calculator 40
  • a emp is the sum of A a and the emphysema area calculated by the emphysema calculator 80).
  • YOLACT learning requires bronchioles labels. Because the size of the bronchioles is relatively small compared to the overall section, we divide them into 5x5 partitions and label the bronchioles directly within these partitions. One partition is 1,640 wide and 1,440 long. The total number of partitions is 5,925, and labeling is performed on 50, 40 are used as train data, and the remaining 10 are used as test data.
  • the bronchial regions within every section are segmented and classified.
  • the interstitial and bronchial regions are colored black. Treats alveoli and emphysema with white color.
  • the total air layer pixel area is extracted by calculating the sum of the pixel areas of the specified labels.
  • OpenCV's erosion is used to perform a step-by-step erosion process on the air layer region.
  • the normal alveoli are removed, leaving only the features of emphysema.
  • the center coordinates of the confirmed emphysema are mapped to the original image extracted in 2-5) to the original lung image. Based on the coordinates of the center, the emphysema area is assigned a color using the BFS (Breadth First Search) algorithm.
  • BFS Bitth First Search
  • the total area of the air layer extracted in 2-3) total air layer area calculation and the area of emphysema extracted in 2-7) emphysema area extraction are calculated in pixel units.
  • the final proportion of emphysema is quantified by calculating the area of emphysema relative to the area of the entire air space.
  • Figures 22 and 23 show the results of emphysema quantified at each stage based on quantification by two experts.
  • the x-axis represents the number of samples, and the y-axis represents the proportion of emphysema for each sample.
  • Figure 22 shows quantitative results based on Expert 1. Samples quantified at the same ratio were grouped and compared. For Expert 1, 0% for samples 1 to 2, 1% for samples 3 to 13, 2% for samples 14 to 20, 5% for samples 21 to 45, 10% for samples 46 to 59, and 1% for samples 60 to 64. It was quantified as 15%, samples 65 to 69 as 20%, and sample 70 as 30%.
  • Figure 23 shows the quantified results based on expert 2. Analyzing the graph in Figure 23, samples quantified at the same ratio were grouped and compared. For Expert 2, Sample 1 was quantified as 2%, Samples 2 to 11 as 5%, Samples 12 to 13 as 7%, Samples 14 to 26 as 10%, and Samples 27 to 35 as 10%. It was quantified as 15%, samples 36 to 54 were 20%, samples 55 to 59 were 25%, samples 60 to 67 were 30%, samples 68 to 69 were 35%, and sample 70 was 50%.
  • the expert's mean, variance, and standard deviation were calculated as shown in Figure 24.
  • Expert 1 showed that the deviation decreased with each step, while expert 2 showed that the deviation increased with each step.
  • Expert 1 quantified the degree of emphysema as low, and Expert 2 quantified it as high.
  • Expert 1 showed the smallest deviation from the results according to the present invention at step 14 and Expert 2 at step 12.
  • emphysema was detected in the same way for all samples and quantified on an absolute basis. Therefore, below, the results according to the present invention are assumed to be standard quantification and the results of expert quantification are compared and analyzed.
  • the stage with the smallest deviation from the quantified results at each stage is selected based on the proportion of emphysema quantified by the expert for each sample.
  • the present invention can present a method for quantifying lung disease in lung samples according to absolute standards using machine learning.
  • the present invention provides a system that can modify quantification decisions based on human experience and intuition based on absolute quantification of lung disease in lung specimens.
  • a bronchiole removal step in which the bronchiole removal unit 20 recognizes a bronchiole in the lung image and then removes the bronchiole.
  • Emphysema detection step in which the emphysema detection unit 70 detects emphysema whose coordinates have been confirmed.
  • An emphysema color designation step in which the emphysema color designation unit 72 assigns a color to the emphysema area based on the coordinates of the center of the emphysema.
  • Emphysema calculation step in which the emphysema calculation unit 80 extracts the detected emphysema and calculates the area.
  • An emphysema area designation step in which the emphysema area designation unit 81 confirms the emphysema area detected by the emphysema detection unit 70 and then labels the emphysema area.
  • An emphysema pixel extraction step in which the emphysema pixel extraction unit 82 extracts the area of the emphysema pixel by calculating the sum of pixels of the labeled emphysema area.
  • Emphysema quantification step in which the emphysema quantification unit 90 quantifies the emphysema rate of the extracted emphysema.

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Abstract

La présente invention concerne un système pour quantifier une maladie pulmonaire d'un échantillon pulmonaire, et un procédé pour quantifier une maladie pulmonaire à l'aide de celui-ci, et, plus particulièrement, un système pour quantifier une maladie pulmonaire d'un échantillon pulmonaire à l'aide d'une vision artificielle et d'un apprentissage automatique, et un procédé pour quantifier une maladie pulmonaire à l'aide de celui-ci, le système détectant une pneumonie et des lésions emphysémateuses à partir d'une image d'un échantillon pulmonaire et l'utilisant pour identifier une incidence de pneumonie et une incidence d'emphysème, ce qui permet une quantification standard d'opinions d'expert.
PCT/KR2023/020600 2022-12-14 2023-12-14 Système de quantification de maladie pulmonaire d'un échantillon pulmonaire à l'aide d'une vision artificielle et d'un apprentissage automatique, et procédé de quantification de maladie pulmonaire à l'aide du système WO2024128818A1 (fr)

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KR1020220174559A KR20240091538A (ko) 2022-12-14 2022-12-14 컴퓨터 비전과 머신러닝을 사용한 폐 검체의 폐렴 정량화 시스템 및 이를 이용한 폐렴 정량화 방법
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