Prediction method of pathological classification of chronic pancreatitis

Abstract

The present invention relates to the field of medical image processing, and in particular to an image processing and classification method for pancreatitis pathological typing, comprising obtaining a pancreatic HE pathological image of a patient with chronic pancreatitis; segmenting the pancreatic HE pathological image to obtain a mask associated with multiple tissues of the pancreas; extracting image blocks based on the mask; and training an image segmentation model using the image blocks to obtain parameters of multiple predetermined features. The pathological typing of pancreatitis is then determined using latent class analysis. The present invention also provides a method for predicting pancreatitis pathological typing, which, by obtaining a pancreatic CT image of a patient with chronic pancreatitis without requiring a invasive incision to acquire the pathological image, can yield a prediction result for the pancreatitis pathological typing. The pathological typing is correlated with the endocrine function, exocrine function, and prognosis of the patient with chronic pancreatitis. The present invention also provides a method for constructing a non-invasive prediction model for pancreatitis pathological typing images, as well as equipment, media, and program products.

Description

Technical Field

[0001] The present invention relates to the field of medical image processing, and in particular to a pancreatitis pathology image processing method, a classification method, a typing prediction model construction method, a prediction method, a device, a medium and a program product. Background Art

[0002] Chronic pancreatitis is a chronic, progressive, irreversible fibroinflammatory disease. Histopathology is the best way to visually demonstrate the severity of the disease and is the gold standard for diagnosis. Due to the scarcity of pathological specimens for chronic pancreatitis, related research is rare. The clinicopathological features of chronic pancreatitis include progressive atrophy of the pancreatic parenchyma, fibrosis, pain response, pancreatic duct tortuosity, stenosis or dilatation, stones, and pancreatic endocrine and endocrine dysfunction. This process gradually worsens with disease progression. Accurate grading of chronic pancreatitis is key to understanding its severity, monitoring its condition, and guiding its treatment.

[0003] Among existing pathological assessment methods for chronic pancreatitis, routine pathology reports often use general categories of mild, moderate, or severe to evaluate the degree of histological changes in chronic pancreatitis. This results in poor diagnostic consistency among doctors, vague evaluation targets, and low credibility. The few pathological studies on the severity of chronic pancreatitis have mostly focused on semi-quantitative assessments of single tissue components. For example, the PF grading method proposed by Kloppel et al. is relatively simple and has a certain correlation with pancreatic function, but it cannot overcome subjective factors and ignores the correlation value between various tissue components. Therefore, it has not been widely recognized and applied in clinical practice. Severity grading systems such as the M-ANNHEIM classification system are mostly based on clinical and imaging, with multiple evaluation indicators and a cumbersome process, making them difficult to popularize and apply in clinical practice.

[0004] A pathological severity grading based on precise quantification of chronic pancreatitis pathology has not yet been proposed. Summary of the Invention

[0005] The present invention provides a pancreatitis pathology image processing method, a classification method, a typing prediction model construction method, a prediction model, a device, a medium and a program product, which can be applied to the prognosis classification diagnosis of chronic pancreatitis.

[0006] An embodiment of the present invention discloses a pancreatic pathology image processing method, the method comprising:

[0007] Obtain pancreatic HE pathological images of patients with chronic pancreatitis;

[0008] Segmenting the pancreatic HE pathological image to obtain a mask associated with a plurality of tissues of the pancreas;

[0009] extracting an image block based on the mask;

[0010] An image segmentation model is trained using the image blocks to obtain parameters of a plurality of predetermined features.

[0011] Optionally, extracting the image block based on the mask includes performing flipping, rotation, Gaussian blurring, and color enhancement processing on the image block, and standardizing the image block.

[0012] Optionally, using the image block to train the image segmentation model includes binarizing the image block, deriving segmentation areas of the multiple tissues by traversing pixel points of the image block, and obtaining parameters of the multiple predetermined features based on the segmentation areas.

[0013] An embodiment of the present invention discloses a pancreatic pathology image classification method, the method comprising:

[0014] Obtain pancreatic HE pathological images of patients with chronic pancreatitis;

[0015] Using the method described above, parameters of a plurality of predetermined features are obtained.

[0016] Based on the parameters of the multiple predetermined features, a latent class analysis is used to determine the pathological classification of pancreatitis, where the pathological classification is related to the endocrine function, exocrine function and prognosis of the chronic pancreatitis patient.

[0017] Optionally, determining the pathological classification of pancreatitis using latent class analysis includes best fitting classification results based on Akaike's Information Criterion (AIC).

[0018] An embodiment of the present invention discloses a method for constructing a pancreatitis pathological typing prediction model, the model construction method comprising:

[0019] Acquiring the first plain scan phase image and portal venous phase image of pancreatic CT images of patients with chronic pancreatitis;

[0020] segmenting the portal phase image to obtain a mask associated with a plurality of tissues of the pancreas;

[0021] training an image segmentation model using the mask and the first plain scan image to obtain a trained image segmentation model;

[0022] inputting the second plain scan image of the patient to be tested into the trained image segmentation model to obtain a pancreatic parenchymal region image of the second plain scan image, and extracting a plurality of radiomics features based on the pancreatic parenchymal region image;

[0023] Predetermined features from the multiple imaging omics features are selected, omics scores are calculated, and a pathology classification prediction model is constructed based on the omics scores.

[0024] Optionally, the training of the image segmentation model using the mask and the first plain scan period image also includes aligning the first plain scan period image and the portal venous phase image, generating a mask corresponding to the first plain scan period image based on the mask, and training the image segmentation model using the mask corresponding to the first plain scan period image.

[0025] Optionally, extracting the radiomics feature based on the pancreatic parenchymal region image includes performing dimensionality reduction processing on the radiomics feature to obtain the predetermined feature.

[0026] An embodiment of the present invention discloses a method for predicting pancreatitis pathological typing, the method comprising:

[0027] Obtain pancreatic CT images of patients with chronic pancreatitis;

[0028] The pancreatitis pathological classification prediction results were obtained based on the pathological classification prediction model, among which,

[0029] The pathological classification prediction model is obtained according to the above-mentioned model construction method.

[0030] An embodiment of the present invention discloses an electronic device, characterized in that the device includes a processor and a memory storing computer-executable instructions, and the processor is configured to execute the instructions to implement the above-mentioned pancreatic pathology image processing method, pancreatic pathology image classification method, pancreatitis pathology classification prediction model construction method or pancreatitis pathology classification prediction method.

[0031] An embodiment of the present invention discloses a computer-readable storage medium, characterized in that the computer-readable storage medium stores at least one computer instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned pancreatic pathology image processing method, pancreatic pathology image classification method, pancreatitis pathology classification prediction model construction method, or pancreatitis pathology classification prediction method.

[0032] An embodiment of the present invention discloses a computer program product, characterized in that the computer program product includes computer instructions, which, when executed, implement the above-mentioned pancreatic pathology image processing method, pancreatic pathology image classification method, pancreatitis pathology classification prediction model construction method, or pancreatitis pathology classification prediction method.

[0033] The pancreatitis pathology image processing method, classification method, typing prediction model construction method, prediction method, device, medium and program product provided by the present invention automatically segment and quantify the pancreatic acinus, stroma, islets, nerves, fat and lymphocytes through deep learning. Tissue delineation is performed on the HE pathology image, the segmentation mask of the delineation is collected, image blocks are extracted based on the segmentation mask, and the segmentation model is trained and verified at the image block level, ultimately achieving the quantification of indicators, including the automatic quantitative extraction of acinar proportion, stroma proportion, islet proportion, nerve proportion, fat proportion, lymphoid structure proportion, acinar-stroma ratio, islet-stroma ratio, acinar-islet ratio and functional cell-stroma ratio. Furthermore, the similarity between the quantitative indicator groups is evaluated through latent class analysis, and a two-category pathological typing is ultimately determined. Since the establishment of the above two classifications relies on pathological results, they still have not broken the inherent limitation of chronic pancreatitis pathology research, namely the scarcity of pathological specimens. Therefore, a model for non-invasive prediction of pathological classification was constructed. The imaging conversion of the above pathological classification was achieved by applying deep learning imaging omics. In the process, an automatic quantitative segmentation method for chronic pancreatitis was developed based on pancreatic CT plain scan images. Multiple quantitative features were extracted, the most relevant main features were screened, and the omics scores were calculated to construct a predictive model for pancreatitis pathological classification.

[0034] Compared with existing technologies, the main difference and effectiveness of the present invention lies in the fact that HE pathology images serve as algorithm input, incurring no additional cost, fundamentally improving the simplicity and universality of segmentation. Furthermore, based on the present invention's pancreatitis pathology classification prediction model, when diagnosing chronic pancreatitis pathology, it is not necessary to obtain stained HE pathology images and enhanced CT images of the patient; only plain CT images of the patient are required. This makes the method non-invasive, without the risk of iodine allergy, inexpensive, and friendly to people with poor liver and kidney function. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of a pancreatic pathology image processing method according to an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a model for establishing pancreatitis pathological typing according to an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of two types of pathological typing according to an embodiment of the present invention;

[0038] Figure 4 This is a flow chart of a method for constructing a pancreatitis pathological typing prediction model according to an embodiment of the present invention;

[0039] Figure 5 is a schematic diagram of a pathological classification prediction model according to an embodiment of the present invention;

[0040] Figure 6 is a hardware structure block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below with reference to specific embodiments and the accompanying drawings. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. In addition, for ease of description, the accompanying drawings only illustrate some, but not all, structures or processes relevant to the present invention. It should be noted that in this specification, similar reference numerals and letters represent similar items in the following drawings.

[0042] It should be understood that although the terms "first," "second," and the like may be used herein to describe various features, these features should not be limited by these terms. These terms are used merely to distinguish and should not be understood as indicating or implying relative importance. For example, a first feature may be referred to as a second feature, and similarly, a second feature may be referred to as a first feature, without departing from the scope of the exemplary embodiments.

[0043] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0044] The clinical and pathological course of chronic pancreatitis gradually worsens as the disease progresses, potentially progressing to pancreatic cancer, a malignant tumor with a low survival rate and high mortality. Accurately grading chronic pancreatitis is crucial for understanding its severity, monitoring the condition, and guiding treatment. However, a quantitative pathological classification for chronic pancreatitis has yet to be established, and histopathological severity assessment methods are severely lacking.

[0045] In order to solve the above problems, an embodiment of the present invention provides a pancreatic pathology image processing method and classification method, which automatically segment and quantify the pancreatic acinus, stroma, islets, nerves, fat and lymphocytes through deep learning. Tissue outlining is performed on the HE pathology image, the segmentation mask of the outline is collected, the image block is extracted based on the segmentation mask, and the segmentation model is trained and verified at the image block level, and finally the quantification of indicators is achieved, including the automatic quantitative extraction of acinar proportion, stroma proportion, islet proportion, nerve proportion, fat proportion, lymphatic structure proportion, acinar stroma ratio, islet stroma ratio, acinar islet ratio and functional cell stroma ratio. Furthermore, the similarity between the quantitative indicator groups is evaluated by latent class analysis, and finally a two-category pathological classification is determined. The evaluation efficiency and stability are improved by automatic quantification, and a new pathological classification is established based on the combined similarity between quantitative indicators. The solution of the present invention is an optimized chronic pancreatitis pathology assessment method.

[0046] Figure 1This is a flowchart of a pancreatic pathology image processing method according to an embodiment of the present invention.

[0047] S110: Acquire a pancreatic HE pathological image of a patient with chronic pancreatitis.

[0048] Pathological HE-stained images are digitized using a slice scanner. In the diagnosis of pancreatitis, pathological diagnosis is considered the "gold standard" for tumor diagnosis.

[0049] The embodiments of the present invention obtain pancreatic pathological sections from patients with chronic pancreatitis to obtain pancreatic HE pathological images. The following steps are explained and illustrated based on the obtained pancreatic HE pathological images.

[0050] S120: Segment the pancreatic HE pathological image to obtain a mask associated with multiple tissues of the pancreas.

[0051] To avoid unnecessary calculations in non-target areas of pancreatic HE images, pancreatic HE pathology images were preprocessed. The pancreatic region of interest (ROI) was manually defined, and the interstitial, acinar, islet, neural, adipose, and lymphatic structures were delineated, and corresponding tissue masks were generated.

[0052] S130: Extracting image blocks based on the mask.

[0053] As an implementation method, multiple tissues of the pancreas are outlined to obtain a mask of the outlined area. Combined with the tissue labels given by the pathologist, for example, stroma, acinar, islet, nerve, fat vacuoles and lymphocytes, image blocks are randomly extracted on the mask, and the obtained image blocks are used as the training set and validation set of the image classification model. The ratio of the training set to the validation set is set to a predetermined ratio according to the specific experimental requirements. The amount of data of the target image block and the non-target image block is kept the same to prevent the problem of class imbalance. When at least one pixel in the image block is classified as the target class, the image block is considered to be the target image.

[0054] In order to better adapt to various staining and acquisition methods, data enhancement can be used. Mask-based image block extraction includes flipping, rotating, Gaussian blurring and color enhancement of image blocks, and standardizing image blocks. For example, "horizontal or vertical flipping", "90-degree rotation", "Gaussian blurring" and "color enhancement" are performed. Among them, color enhancement includes random changes in brightness, contrast, hue and saturation. After data enhancement processing, the image is quickly standardized.

[0055] S140: Using the image blocks to train an image segmentation model, and obtaining parameters of a plurality of predetermined features.

[0056] The hybrid loss function used to build the image segmentation model consists of a cross-entropy loss and an intersection-over-union loss. The DeepLabV3+ model was used for training. DeepLabV3+ utilizes dilated convolutions with varying rates to capture multi-scale context. DeepLabV3+ extends this by transferring low-level features from the encoder to the decoder. For the first 50 iterations, only the decoder weights are trained. For the remaining iterations, both the encoder and decoder are trained. The learning rate is deterministically decayed every few iterations to ensure gradual model convergence.

[0057] refer to Figure 2 , the image segmentation model 210 is trained using HE images. The HE images are divided into a training set and a validation set. Both the training set and the validation set use image blocks to train the image segmentation model 210, including a binary image block. The pixel points of the image block are traversed to obtain the segmentation areas of multiple tissues, and the parameters of multiple predetermined features are obtained based on the segmentation areas.

[0058] In one example, an image segmentation model 210 is trained based on image blocks serving as a training set. Image blocks serving as a validation set are then fed into the trained image segmentation model 210 to obtain segmentation results. Specifically, image blocks corresponding to multiple tissues are binarized into black and white regions with values ​​of 0 and 255. Each pixel in the white region is traversed, and the total number of pixels is calculated to determine the segmented area of ​​the tissue components. The white region represents the segmentation target with a tissue label, while the black region represents the remaining components. For example, by accurately segmenting multiple tissues in chronic pancreatitis, the quantified areas of stroma, acinar, islet, nerve, fat, and lymphatic structures in pancreatic HE pathology sections are obtained. To enhance comparability, considering the differences in slice size and tissue content, the six target components are compared with the pancreatic tissue area in the pancreatic HE pathology images. The respective percentages of the six components, including acinar, stroma, islet, fat, nerve, and lymphatic structures, are obtained. Furthermore, to explore the relationship between acinar, stroma and islets, four additional correlation ratios were included, including the acinar-stroma ratio, islet-stroma ratio, acinar-islet ratio and functional cell (acinar + islet)-stroma ratio, and 10 types of pathological quantitative indicators were automatically extracted.

[0059] Figure 2 Schematic diagram of a model for establishing pancreatitis pathological typing according to an embodiment of the present invention.

[0060] The pancreatic pathology image classification method of the present invention obtains pancreatic HE pathology images from patients with chronic pancreatitis. Based on multiple predetermined feature parameters obtained through a pancreatic pathology image processing method, latent class analysis is used to classify the pancreatic pathology images and determine the pathological type of pancreatitis. The classification basis, as verified below, indicates that the pathological type is correlated with the endocrine and exocrine function and prognosis of patients with chronic pancreatitis.

[0061] Image classification system 200, based on the pancreatic pathology image processing and classification methods of the present invention, includes an image segmentation model 210 and a latent class analysis module. System 200 is used to determine the pathological classification of pancreatitis. Image segmentation model 210 receives a pancreatic HE pathology image from a patient with chronic pancreatitis and outputs a segmentation result. Based on multiple predetermined feature parameters of the segmentation result, latent class analysis is used to determine the pathological classification of pancreatitis.

[0062] Latent class analysis is a statistical analysis technique that combines latent variable theory with categorical variables. Through the Latent Class Model (LCM), the relationships between explicit categorical variables are explained using internal latent categorical variables, allowing the relationships between explicit variables to maintain their local independence after being estimated by the latent categorical variables. Latent class analysis compares the similarities between the parameters of multiple predetermined features to establish a classification, allowing multiple categorical variables to generate a new categorical variable, which then divides the population into several groups.

[0063] In one embodiment, the latent class analysis is used to determine the pathological classification of pancreatitis, including the best-fit classification result based on the Akaike information criterion (AIC). In addition, the Bayesian information criterion (BIC) can also be used as a discriminant indicator of classification fit.

[0064] In one example, latent class analysis was performed on the segmentation results of pathological HE images from each patient with chronic pancreatitis. To determine the number of latent class classifications, classification attempts were made, ranging from one to several. The number of classes corresponding to the minimum AIC and BIC values ​​was selected to establish the pancreatitis pathological classification. The population was then divided into groups based on the results of the latent class analysis.

[0065] Figure 3 Schematic diagram of two types of pathological typing according to an embodiment of the present invention.

[0066] The image segmentation model 210 obtains parameters for multiple predetermined features. Latent class analysis is then used to determine the pathological classification of pancreatitis based on these parameters. To validate their clinical relevance, the pathological classification is correlated with the endocrine function, exocrine function, and prognosis of patients with chronic pancreatitis. First, the image segmentation model 210 obtains parameters for multiple predetermined features. As an example, pancreatitis patients include those with chronic pancreatitis and those with recurrent acute pancreatitis. 181 patients with chronic pancreatitis were included as sample data for this study. Pancreatic HE pathology images from 19 of these patients were repeatedly selected for segmentation training. Both the training and validation sets consisted of image blocks derived from the segmentation masks of pancreatic HE pathology images. The DeeplabV3+ network was used to automatically segment multiple pancreatic tissue components in these pancreatic HE pathology images. The basic component ratios of six pancreatic tissue components were extracted. To further explore the correlation between these feature parameters and pancreatic function, four correlation ratios between acinar, stroma, and islet structures were also obtained, totaling 10 pathological parameter categories. The 10 feature parameters obtained from the segmentation results are shown in Table 1.

[0067] Table 1 Image segmentation results

[0068]

[0069]

[0070] Combining the data in Table 1, three pathological types related to prognosis were identified, as shown in Table 2. Furthermore, the similarity between multiple characteristic parameters was evaluated by latent class analysis, and a two-category pathological type was determined with seven parameters. Figure 3 There are two pathological types of leukemia: type I and type II.

[0071] Table 2 Number of classifications

[0072] Number of categories Log-likelihood function BIC 1 -1918.84 (df=12) 3900.05 2 -1806.97 (df=25) 3743.91 3 -1777.25 (df=38) 3752.04

[0073] Type I patients were characterized by a high acinar ratio, a low stromal ratio, a low tertiary lymphoid ratio, a high acinar-stromal ratio, a high islet-stromal ratio, a high acinar-islet ratio, and a high functional cell (acinar + islet)-stromal ratio. Type II patients were characterized by a low acinar ratio, a high stromal ratio, a high tertiary lymphoid ratio, a low acinar-stromal ratio, a low islet-stromal ratio, a low acinar-islet ratio, and a low functional cell-stromal ratio. Regression models revealed a significant correlation between pathological type and pancreatic endocrine and exocrine function. The risk of endocrine or exocrine insufficiency in type II patients was 4.2 times and 3.8 times higher than in type I patients, respectively.

[0074] Multivariate regression analysis was used to evaluate the correlation between the classification of chronic pancreatitis and pancreatic exocrine function and endocrine function. The pancreatic exocrine function was significantly correlated with the latent pathological classification of chronic pancreatitis, and the pancreatic endocrine function was significantly correlated with the latent pathological classification of chronic pancreatitis.

[0075] It is worth noting that those skilled in the art will understand that although the above-mentioned pathological classification is based on pathological results, in view of the scarcity of pathological specimens, in order to popularize the pathological classification in clinical practice and improve the severity evaluation system of chronic pancreatitis, the following examples will combine pancreatic CT cross-sectional imaging and deep learning imaging genomics to non-invasively predict pancreatitis pathological classification as a supplementary solution to the pancreatitis pathological classification based on pathological HE images, which can verify and predict the pathological classification. See below for details. Figure 4 and Figure 5 The embodiments in the context are a complete invention concept formed around pathological typing and should not be viewed separately.

[0076] Figure 4 The present invention is a flowchart of a method for constructing a pancreatitis pathological typing prediction model according to an embodiment of the present invention.

[0077] Step S410: Acquire a first plain scan phase image and a portal venous phase image of a pancreatic CT image of a patient with chronic pancreatitis.

[0078] In an embodiment of the present invention, chronic pancreatitis patients include chronic pancreatitis patients and recurrent acute pancreatitis patients, among which acute pancreatitis patients are regarded as mildly suspected chronic pancreatitis patients and are no longer distinguished here and are collectively referred to as chronic pancreatitis patients. Chronic pancreatitis patients for whom pancreatic CT images are obtained are patients without pathology.

[0079] The limitations of enhanced CT in the diagnosis of chronic pancreatitis include the high X-ray dose, the risk of iodine allergy, the fact that patients with impaired liver and kidney function are not suitable for enhanced CT examinations, and the high cost. Unenhanced CT, which does not require the injection of contrast agents, is noninvasive, convenient, rapid, and economical.

[0080] In enhanced CT scans, contrast agent is typically injected intravenously into the blood vessels, where it flows through the bloodstream to various organs and lesions. Plain scan images are CT scan images acquired before the contrast agent is developed. Contrast agent development consists of three phases: the arterial phase, the portal phase, and the delayed phase. The arterial phase refers to the period during which the arterial vessels fill and develop, demonstrating the characteristics of the contrast agent flowing into the arteries. The portal phase refers to the period during which the portal vein fills and develops, demonstrating the characteristics of blood flowing through the portal vein after the arterial phase. The delayed phase describes the characteristics that occur after the arterial and portal phases.

[0081] Step S420: Segment the portal venous phase image to obtain a mask associated with multiple tissues of the pancreas.

[0082] Combine Figure 5 The image classification system uses the image segmentation model 510 to segment the delineated portal phase image to obtain multiple tissue-related segmentation masks, such as masks of pancreatic parenchyma, pancreatic duct, stones, stents, pseudocysts, and pancreatic head, body, and tail.

[0083] In the preprocessing stage of image segmentation, the delineated tissue mask is extracted, the image block size is adjusted, and normalization and enhancement are performed. The enhancement methods include rotation, scaling, Gaussian noise, and Gaussian blur.

[0084] Step S430: using the mask and the first plain scan period image to train the image segmentation model to obtain a trained image segmentation model.

[0085] As an embodiment, using the mask and the first plain scan period image to train the image segmentation model 510 also includes aligning the first plain scan period image and the portal venous phase image, generating a mask corresponding to the first plain scan period image based on the mask, and training the image segmentation model 510 using the mask corresponding to the first plain scan period image.

[0086] Because some structures in the CT plain phase image are not clear and difficult to identify, the pancreatic parenchyma, pancreatic duct, stones, stents, pseudocysts and pancreatic head, body and tail partitions must first be outlined on the portal venous phase image, a mask is generated based on the outlined annotations, the plain phase image and the portal venous phase image are aligned, the mask outlined on the portal venous phase image is transferred to the plain phase image, and the image segmentation model 510 is trained based on the mask corresponding to the plain phase image.

[0087] In the above embodiment, the first plain scan image and the portal venous phase image are subjected to image registration, and the registration principle is based on the portal venous phase image. In other embodiments, the plain scan image, arterial phase image, portal venous phase image, and delayed phase image can also be calibrated based on the portal venous phase image.

[0088] Because the first plain scan image has been registered with the portal venous phase image, a mask corresponding to the first plain scan image can be generated based on the annotations of the segmented mask. The image segmentation model is trained based on the first plain scan image and the masks of the pancreas, stones, and pancreatic duct therein, yielding a trained image segmentation model 510. Thus, the trained image segmentation model 510 is a segmentation model based on the plain scan image.

[0089] In some embodiments, the image segmentation model 210 and the image segmentation model 510 are obtained based on uuU-Unet deep learning. The Unet network mainly includes two parts, the first half is for feature extraction, and the second half is for upsampling. Using the mask and the plain scan images of the pancreatic CT images of patients with chronic pancreatitis to train the image segmentation model 510, the trained image segmentation model 510 also includes obtaining the plain scan images of the pancreatic CT images of patients with chronic pancreatitis, training the image segmentation model 210 based on the mask and the plain scan images of the pancreatic CT images of patients with chronic pancreatitis, that is, using the mask and the plain scan images of the pancreatic CT images of patients with chronic pancreatitis as a training set, training the image segmentation model 510 with the training set, and separately finding a batch of plain scan images of the pancreatic CT images of patients with chronic pancreatitis as a validation set, and validating the image segmentation model 210 with the validation set. For details, refer to step S440.

[0090] Step S440: Input the second plain scan image of the patient to be tested into the trained image segmentation model to obtain the pancreatic parenchymal area image of the second plain scan image, and extract multiple imaging omics features based on the pancreatic parenchymal area image.

[0091] As an embodiment, extracting radiomics features based on the pancreatic parenchymal region image includes performing dimensionality reduction processing on the radiomics features to obtain predetermined features.

[0092] The parenchyma and pancreatic duct are key areas for feature extraction. Segmentation helps eliminate other interfering factors within the region, improving the stability and reliability of feature extraction. Image segmentation removes pancreatic stones, stents, pancreatic ducts, and pseudocysts, revealing the pancreatic parenchyma region. Radiomic features are extracted from images of the pancreatic parenchyma region and then filtered through dimensionality reduction to obtain predetermined features, which serve as primary features.

[0093] In one example, features with correlation coefficients > 0.9 were screened out through correlation analysis (Spearman), and then the least absolute shrinkage and selection operator logistic regression method was used to screen out the most relevant features.

[0094] Step S450: Select predetermined features from multiple imaging omics features, calculate omics scores, and construct a pathology classification prediction model based on the omics scores.

[0095] A deep learning radiomics score calculation formula was established based on predetermined features, and the typing threshold was determined by maximizing the Youden index, and then a regression model was formed.

[0096] In one example, because the extracted automated quantitative indicators did not differ significantly between pathological types, a deep learning radiomics score calculation formula was established based solely on features derived from the pancreatic parenchyma and pancreatic duct. The analyte value corresponding to the maximum Youden index was selected as the classification threshold. Pancreatitis patients with values ​​below this threshold were predicted to have type I, while those with values ​​above this threshold were predicted to have type II.

[0097] Figure 5 Schematic diagram of a pathology classification prediction model according to an embodiment of the present invention.

[0098] The pancreatitis case classification prediction method of the present invention obtains pancreatic CT images of patients with chronic pancreatitis and obtains pancreatitis pathological classification prediction results based on the pathological classification prediction model obtained by the pancreatitis pathological classification prediction model construction method.

[0099] Image classification system 500, based on the pancreatitis pathology classification prediction model construction method and the pancreatitis pathology classification prediction method of the present invention, includes an image segmentation model 510 and a pathology classification prediction model 520. Image segmentation model 510 can receive a CT scan of a patient to be tested and output a pancreatic mask. Pathology classification prediction model 520 determines the pathology classification of the CT scan image based on the pancreatic mask.

[0100] Figure 5 Provided is an end-to-end image classification system 500, in which the CT plain scan images of the patient to be tested are input into an image segmentation model, and the output of the image segmentation model is input into a pathology classification prediction model. There is no need to obtain the patient's pancreatic pathology slice images or pancreatic enhanced CT images. Only the patient's CT plain scan images need to be obtained. Existing non-pathological enhanced CT images are used in combination with deep learning to achieve pancreatitis pathology classification prediction based on non-enhanced CT images. The system has the advantages of being non-invasive, having no risk of iodine allergy, being inexpensive, and being friendly to people with poor liver and kidney function.

[0101] The image classification model 510 is obtained through the following method.

[0102] First, the first plain scan phase image and portal venous phase image of the pancreas CT image of the patient with chronic pancreatitis are acquired;

[0103] Second, the portal venous phase image was segmented to obtain masks associated with multiple tissues of the pancreas;

[0104] Again, the mask and the first plain scan period image are used to train the image segmentation model to obtain a trained image segmentation model.

[0105] Based on the parameters of multiple predetermined features output by the image segmentation model 510, with the assistance of the pathology classification prediction model 520, the segmentation and classification results can be combined with pathology, clinical, and CT features to provide a final pathology classification prediction result.

[0106] The pathology classification prediction model 520 classifies the plain scan image according to the pancreas mask. The pathology classification prediction model 520 is trained by the following method.

[0107] First, the second plain scan image of the patient to be tested is input into the trained image segmentation model 510 to obtain the pancreatic parenchymal area image of the second plain scan image, and multiple imaging omics features are extracted based on the pancreatic parenchymal area image.

[0108] In addition, predetermined features are selected from a plurality of imaging omics features, omics scores are calculated, and a pathology classification prediction model 520 is constructed based on the omics scores.

[0109] Using CT images without pathology as a test set, the clinical applicability of the pathology classification prediction model was validated. Correlation analysis revealed significant differences between pancreatic endocrine and exocrine function in both predicted types I and II. The risk of endocrine and exocrine insufficiency in patients with predicted type II was 2.5 times higher than in patients with predicted type I, respectively. This pathology classification prediction model accurately reflects disease severity and exhibits a significant correlation with pancreatic endocrine and exocrine function, demonstrating practical clinical significance.

[0110] Figure 6 FIG. 4 is a block diagram of the hardware structure of an electronic device according to an embodiment of the present invention.

[0111] like Figure 6 As shown, the electronic device 600 may include one or more processors 602, a system control logic 608 connected to at least one of the processors 602, a system memory 604 connected to the system control logic 608, a non-volatile memory (NVM) 606 connected to the system control logic 608, and a network interface 610 connected to the system control logic 608.

[0112] The processor 602 may include one or more single-core or multi-core processors. The processor 602 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In an embodiment of the present invention, the processor 602 may be configured to execute the following operations: Figure 1 The pancreatic pathology image processing method shown and Figure 4 The method for constructing a predictive model for pancreatitis pathological classification is shown.

[0113] In some embodiments, system control logic 608 may include any suitable interface controller to provide any suitable interface to at least one of processors 602 and / or any suitable device or component in communication with system control logic 608 .

[0114] In some embodiments, the system control logic 608 may include one or more memory controllers to provide an interface to the system memory 604. The system memory 604 may be used to load and store data and / or instructions. In some embodiments, the system memory 604 of the electronic device 600 may include any suitable volatile memory, such as a suitable dynamic random access memory (DRAM).

[0115] The non-volatile memory 606 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, the non-volatile memory 606 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of an HDD (Hard Disk Drive), a CD (Compact Disc) drive, and a DVD (Digital Versatile Disc) drive.

[0116] The non-volatile memory 606 may include a portion of storage resources installed on the device of the electronic device 600, or it may be accessible to the device but not necessarily a part of the device. For example, the non-volatile memory 606 may be accessed over a network via the network interface 610.

[0117] In particular, the system memory 604 and the non-volatile memory 606 may respectively include a temporary copy and a permanent copy of the instructions 620. The instructions 620 may include instructions that, when executed by at least one of the processors 602, cause the electronic device 600 to perform the following operations: Figure 1 The pancreatic pathology image processing method shown and Figure 4 Instructions of the processing method of the method for building a pancreatitis pathology classification prediction model are shown. In some embodiments, the instructions 620, hardware, firmware and / or software components thereof may be additionally / alternatively placed in the system control logic 608, the network interface 610 and / or the processor 602.

[0118] The network interface 610 may include a transceiver for providing a radio interface for the electronic device 600, thereby communicating with any other suitable devices (e.g., front-end modules, antennas, etc.) via one or more networks. In some embodiments, the network interface 610 may be integrated with other components of the electronic device 600. For example, the network interface 610 may be integrated with at least one of the processor 602, the system memory 604, the NVM 606, and a firmware device (not shown) having instructions. When at least one of the processors 602 executes the instructions, the electronic device 600 implements Figure 1 and Figure 4 One or more of the various embodiments shown.

[0119] The network interface 610 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 610 may be a network adapter, a wireless network adapter, a telephone modem, and / or a wireless modem.

[0120] In one embodiment, at least one of the processors 602 may be packaged together with one or more controllers for the system control logic 608 to form a system-in-package (SiP). In one embodiment, at least one of the processors 602 may be integrated on the same die with one or more controllers for the system control logic 608 to form a system-on-chip (SoC).

[0121] Electronic device 600 may further include an input / output (I / O) device 612 connected to system control logic 608. I / O device 612 may include a user interface to enable a user to interact with electronic device 600; peripheral component interfaces may also be designed to enable peripheral components to interact with electronic device 600. In some embodiments, electronic device 600 may also include a sensor for determining at least one of environmental conditions and location information related to electronic device 600.

[0122] In some embodiments, input / output (I / O) devices 612 may include, but are not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), speakers, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., an LED flash), and a keyboard.

[0123] In some embodiments, the peripheral component interface may include, but is not limited to, a non-volatile memory port, an audio jack, and a power interface.

[0124] It should be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 600. In other embodiments of the present application, the electronic device 600 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0125] Program code can be applied to input instructions to perform the functions described herein and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a system for processing instructions including processor 602 includes any system having a processor such as a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0126] Program code can be implemented with a high-level programming language or an object-oriented programming language to communicate with the processing system. Where necessary, program code can also be implemented in assembly language or machine language. In fact, the mechanism described in the present invention is not limited to the scope of any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0127] According to one embodiment of the present invention, a computer-readable storage medium is also proposed, in which at least one computer instruction is stored. The at least one instruction is loaded and executed by a processor to implement the aforementioned pancreatic pathology image processing method, pancreatic pathology image classification method, pancreatitis pathology classification prediction model construction method, or pancreatitis pathology classification prediction method.

[0128] According to one embodiment of the present invention, a computer program product is also proposed, which includes computer instructions. When the computer instructions are executed, the aforementioned pancreatic pathology image processing method, pancreatic pathology image classification method, pancreatitis pathology classification prediction model construction method or pancreatitis pathology classification prediction method are implemented.

[0129] Illustrative embodiments of the present invention include, but are not limited to, a pancreatitis pathology image processing method, a classification method, a typing prediction model construction method, a prediction method, an apparatus, a medium, and a program product.

[0130] The various aspects of the illustrative embodiments will be described using terms commonly employed by those skilled in the art to convey the essence of their work to others skilled in the art. However, it will be apparent to those skilled in the art that some alternative embodiments may be implemented using some of the features described. For purposes of explanation, specific numbers and configurations have been set forth to provide a more thorough understanding of the illustrative embodiments. However, it will be apparent to those skilled in the art that alternative embodiments may be implemented without the specific details. In some other cases, well-known features have been omitted or simplified herein to avoid obscuring the illustrative embodiments of the present invention.

[0131] Furthermore, various operations will be described as multiple, separate operations in a manner that is most helpful for understanding the illustrative embodiments; however, the order of description should not be construed to imply that the operations must be performed in order of description, and many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when the described operations are completed, but can also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subprogram, and the like.

[0132] References in the specification to "an example," "an example," "an embodiment," "an implementation," etc., indicate that the described embodiment may include a particular feature, structure, or property, but that every embodiment may or may not necessarily include the particular feature, structure, or property. Moreover, these phrases are not necessarily referring to the same embodiment. Furthermore, when particular features are described in conjunction with a specific embodiment, the knowledge of those skilled in the art can influence how these features can be combined with other embodiments, whether or not those embodiments are explicitly described.

[0133] Unless the context dictates otherwise, the terms "comprising," "having," and "including" are synonymous. The phrase "A and / or B" means "(A), (B), or (A and B)."

[0134] As used herein, the term "module" may refer to, be part of, or include: memory (shared, dedicated, or group) for running one or more software or firmware programs, application-specific integrated circuits (ASICs), electronic circuits and / or processors (shared, dedicated, or group), combinational logic circuits, and / or other suitable components that provide the functionality.

[0135] In the accompanying drawings, some structural or method features may be shown in a particular arrangement and / or order. However, it should be understood that such a particular arrangement and / or order is not required. Rather, in some embodiments, these features may be illustrated in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular drawing does not mean that all embodiments need to include such features. In some embodiments, these features may not be included or may be combined with other features.

[0136] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0137] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0138] Those skilled in the art will appreciate that the modules in the devices of the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0139] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

Claims

1. A method for constructing a prediction model for pancreatitis pathological typing, characterized in that: The model building method comprises: Acquiring the first plain scan phase image and portal venous phase image of pancreatic CT images of patients with chronic pancreatitis; segmenting the portal phase image to obtain a mask associated with a plurality of tissues of the pancreas; training an image segmentation model using the mask and the first plain scan image to obtain a trained image segmentation model; inputting the second plain scan image of the patient to be tested into the trained image segmentation model to obtain a pancreatic parenchymal region image of the second plain scan image, and extracting a plurality of radiomics features based on the pancreatic parenchymal region image; Selecting predetermined features from the plurality of imaging omics features, calculating omics scores, and constructing a pathology classification prediction model based on the omics scores, The predetermined features include acinar ratio, stroma ratio, tertiary lymphoid structure ratio, acinar stroma ratio, islet stroma ratio, acinar islet ratio and functional cell stroma ratio. The pathological classification prediction model determines the case classification of the two categories based on the multiple predetermined features. Among them, the two categories of case classification include type I patients and type II patients. The pathological characteristics of type I patients are high acinar ratio, low stromal ratio, low tertiary lymphoid structure ratio, high acinar stromal ratio, high islet stromal ratio, high acinar-islet ratio and high functional cell-stromal ratio; the pathological characteristics of type II patients are low acinar ratio, high stromal ratio, high tertiary lymphoid structure ratio, low acinar stromal ratio, low islet stromal ratio, low acinar-islet ratio and low functional cell-stromal ratio.

2. The method according to claim 1, characterized in that The method of training the image segmentation model using the mask and the first plain scan period image also includes aligning the first plain scan period image and the portal venous phase image, generating a mask corresponding to the first plain scan period image based on the mask, and training the image segmentation model using the mask corresponding to the first plain scan period image.

3. The method according to claim 1, characterized in that Extracting the radiomics feature based on the pancreatic parenchymal region image includes performing dimensionality reduction processing on the radiomics feature to obtain the predetermined feature.

4. A method for predicting pancreatitis pathological typing, characterized in that: The method comprises: Obtain pancreatic CT images of patients with chronic pancreatitis; The pancreatitis pathological classification prediction results were obtained based on the pathological classification prediction model, among which, The pathological classification prediction model is obtained according to the model construction method according to claim 1.

5. An electronic device, characterized in that: The device includes a processor and a memory storing computer-executable instructions, and the processor is configured to execute the instructions to implement the pancreatitis pathological typing prediction model construction method according to claim 1 or the pancreatitis pathological typing prediction method according to claim 4.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one computer instruction, which is loaded and executed by the processor to implement the pancreatitis pathological typing prediction model construction method according to claim 1 or the pancreatitis pathological typing prediction method according to claim 4.

7. A computer program product, characterized in that The computer program product includes computer instructions, and when the computer instructions are executed, the method for constructing a pancreatitis pathological typing prediction model according to claim 1 or the method for predicting pancreatitis pathological typing according to claim 4 is implemented.

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