CN105653858A - Image omics based lesion tissue auxiliary prognosis system and method - Google Patents
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- 238000012795 verification Methods 0.000 abstract 1
- 201000010536 head and neck cancer Diseases 0.000 description 12
- 208000014829 head and neck neoplasm Diseases 0.000 description 12
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Abstract
The invention discloses an image omics based lesion tissue auxiliary prognosis system and method. The method comprises the steps of extracting image data of lesion parts with an automatic or manual segmentation method from a big-data-volume patient image database; according to segmentation results of lesion part images, extracting image phenotypic characteristics of each lesion part, and finishing feature extraction of image data of all lesion parts in the patient image database; and based on characteristic data and clinical information data of each lesion part, performing training data set and test data set classification on the data in the patient image database, performing pathologic analysis, clinical stage analysis, gene mutation prediction and survival time prediction of the lesion parts in the training data set with a computer automatic identification method, and performing verification in the test data set. The method is capable of performing qualitative and quantitative prediction analysis on a specific individual and providing credible prediction and analysis results.
Description
Technical field
The present invention relates to medical diagnosis on disease ancillary technique field, relate more specifically to the auxiliary prognosis system of a kind of pathological tissues based on image group and method.
Background technology
Medical image, as the noninvasive early diagnosis of tumor method of one, has been widely used in the auxiliary diagnosis of all kinds of cancer. Using image information to carry out the subjective experience that clinical assistant diagnosis often relies on doctor at present, the patient disease's image feature reflected by image gives corresponding diagnosis. But in medical image still untapped announcement pathology by stages with the valuable information of prognosis.
Dissimilar tumour due to the performance of its pathological characteristics on image totally different, different tumor imaging features also imply that therapeutic modality is completely different, and directly affects prognosis. The anticipation at present realizing tumour by imaging methods all needs doctor to carry out detailed detection according to the clinical experience of its subjectivity, pathological section and blood examination etc. to obtain clinical detection result. But, study based on existing medical image signature analysis, some multidimensional texture feature can accurately reflect the pathology information of pathological tissues, a complete feature database for realizing individuation medical treatment, there is important researching value, so can provide more fully Data support for the screening of follow-up key feature. Therefore employing computer approach has been assisted the forecast analysis of pathology and has been provided believable suggestion and have extremely high Practical significance.
Summary of the invention
For above-mentioned technical problem, it is an object of the invention to provide the auxiliary prognosis system of a kind of pathological tissues based on image group and method.
In order to realize above-mentioned purpose, as an aspect of the present invention, the present invention provides the auxiliary method of prognosis of a kind of pathological tissues based on image group, comprising:
Step S101, from, patient's image database of big data quantity, adopting the dividing method of automatic or manual to extract the image data of diseased region;
Step S102, according to the segmentation result of described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, and the feature completing all diseased region image data in described patient's image database is extracted;
Step S103, based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate in described test data and realize checking.
As another aspect of the present invention, present invention also offers a kind of pathological tissues based on image group and assist pre-examining system, it is characterised in that, comprising:
From, patient's image database of big data quantity, adopting the unit of the image data of the dividing method extraction diseased region of automatic or manual;
Segmentation result according to described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, completes the unit that the feature of all diseased region image data in described patient's image database is extracted;
Based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate the unit realizing checking in described test data.
Known based on technique scheme, the auxiliary method of prognosis of the pathological tissues of the present invention can according to the segmentation result of clinical image data, set up pathology image phenotypic characteristic storehouse, clinical case data are divided into training dataset and test data set by employing Computer Automatic Recognition and respectively method, in training dataset, the different pathological of all kinds of pathology is showed, clinical stages, feature database corresponding to gene mutation type is trained respectively, the prediction of each feature is calculated from original phenotype feature database, prognosis contribution degree, selection can correctly identify that different pathological shows, the key feature of different clinical stages and different genes mutation type, and use the key feature obtained that pathological tissues is carried out pathological manifestations, the prediction of clinical stages and gene mutation type and lifetime, specific individuality is carried out qualitative and quantitative forecast analysis respectively, believable predict and analysis result is provided.
Accompanying drawing explanation
Fig. 1 is the schema of the auxiliary method of prognosis of the pathological tissues based on image group of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in further detail.
The present invention discloses the auxiliary method of prognosis of a kind of pathological tissues based on image group, and its general plotting is: first adopts swollen block target area, the location of dividing method manually or automatically image, completes the segmentation of target image data; Extract the polymorphic type feature of swollen block according to target image data, set up complete phenotypic characteristic storehouse; Basic clinical information according to each patient in image database obtains the information such as patient tissue biopsy result, gene type and survival time, adopt Computer Automatic Recognition and sorting technique the feature of the pathological manifestations of pathological tissues, clinical stages, gene mutation type to be trained respectively and classify, set up prediction prognosis model reliably; It is applied to test data and realizes the prediction respectively to pathological manifestations, clinical stages and gene mutation type with other independent data; Pathology Overall survival and image feature are carried out correlation analysis, thus obtains relation between the survival time of prognosis and iconography feature, patient is provided the qualitative and quantitative prognosis suggestion of individuation.
The auxiliary prognosis system of the pathological tissues based on image group of the present invention and method realize the correlation analysis of image and prognosis by computer software and algorithm, thus disclose the relation between prognosis information and image performance, export quantitative analytical results; Input image data are carried out automatic business processing with the computer software of a set of maturation by it, based on prior model, the pending data of input are carried out personalized analysis, thus pathology prognosis is carried out auxiliary direction. This system and method is not the diagnosis directly realizing disease, but image data gives quantitative analysis and provides personalized assistant analysis and reference, provides Data support to further the diagnosis of doctor.
The objectives of the present invention are as follows: (1) realizes the accurate segmentation of pathological tissues focus area image, pathological tissues carries out location and tumor imaging automatically and extracts, it is achieved the repeatability of diseased region segmentation and accuracy; (2) carrying out swollen block image feature according to pathological tissues target image to extract, the degree of depth excavates each type image feature, sets up complete pathological tissues image property data base; (3) based on the clinical case data of big data, in conjunction with each clinical information and the swollen block image feature of patient, computer automatic sorting identification algorithm is adopted, it is achieved the predictions such as the pathological analysis of pathological tissues, clinical stages analysis and lifetime; And explain the potential relation of pathological tissues gene mutation type and image feature, it is provided that qualitative and quantitative prognosis suggestion.
More specifically, the pathological tissues based on image group of the present invention assists method of prognosis, is a kind of pathological tissues analyses and prediction householder method based on image group (Radiomics), as shown in Figure 1, comprises the following steps:
Step S101, from, patient's image database of big data quantity, adopting the dividing method of automatic or manual to extract the image data of diseased region;
Step S102, according to the segmentation result of described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, and the feature completing all diseased region image data in described patient's image database is extracted;
Step S103, based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate in described test data and realize checking.
Wherein, described image data are the image data that CT, PET, mr or ultrasonograph equipment collect.
Wherein, described diseased region comprises lung, liver or renal tissue.
Wherein, in step S101, described automatic dividing method is by the computer implemented method based on region growing, based on the dividing method of level collection or the dividing method that cuts based on figure.
Wherein, in step S102, described image feature comprises: the swollen block gray feature of the shape feature of diseased region, the textural characteristics of diseased region and/or diseased region.
Wherein, described in step S103, the step of the classification that data in patient's image database carry out training dataset and test data set is comprised:
Adopt computer automatic sorting recognition methods, in conjunction with knowledge of statistics and instrument, set up and analyze the statistics correlation models of image feature and patient clinical information.
Wherein, adopting in the step of prediction that computer automatic identification method carries out the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset described in step S103, described computer automatic identification method needs data comprise to be processed: result, the gene mutation type and/or follow up a case by regular visits to survival time by stages of pathology hypotype, clinical stages and TNM residing for the clinical information of described patient, biopsy result, gene information and survival time, patient clinical data, patient.
The invention also discloses a kind of pathological tissues based on image group and assist pre-examining system, comprising:
From, patient's image database of big data quantity, adopting the unit of the image data of the dividing method extraction diseased region of automatic or manual;
Segmentation result according to described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, completes the unit that the feature of all diseased region image data in described patient's image database is extracted;
Based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate the unit realizing checking in described test data.
Wherein, described image data are the image data that CT, PET, mr or ultrasonograph equipment collect.
Wherein, described automatic dividing method is by the computer implemented method based on region growing, based on the dividing method of level collection or the dividing method that cuts based on figure; Described image feature comprises: the swollen block gray feature of the shape feature of diseased region, the textural characteristics of diseased region and/or diseased region.
Describe the present invention below in conjunction with the head and neck cancer area of computer aided method of prognosis based on CT image.
Step S0: database is determined. The retrospective head and neck cancer patient's CT image included 200 example underwent operative pathology in and confirm, 100 examples are as training case, and 100 examples are as checking case; Data should comprise different TNM by stages, pathological staging, gene mutation type and follow up a case by regular visits to survival time etc. Image sequence comprises: scan mode: unenhanced, enhancing scanning; Often kind of scan mode includes conventional 5mm thickness and 1.25mm thickness; Comprise mediastinum window and soft tissue window.
Step S1: the CT image of head and neck cancer patient in database is carried out efficiently, the segmentation of stable and automatization. Adopt a kind of head and neck cancer lesion region automatic division method based on region growing that pathological tissues in database completes segmentation. Algorithm adopts the region growing of a kind of adaptive threshold automatically to split algorithm based on initial seed point, it is achieved the accurate identification of lesion locations, and self-adaptative adjustment partitioning parameters obtains lesion region image data. The profile information of lesion region is finally adopted to complete image boundary level and smooth, it is possible to realize the good real-time of segmentation and robustness.
Step S2: the feature that the head and neck cancer CT image data after segmentation carry out polymorphic type is extracted. First-order statistical properties and multidimensional statistics feature can be divided into according to feature place Spatial Dimension different images feature; When extracting according to feature based on different directions and different step-length can extract the image feature of yardstick multi-direction, many. Emphasis extracts various dimensions and multidirectional textural characteristics, extract the higher-dimension textural characteristics such as the gray level co-occurrence matrixes three-dimensional space, run-length matrix from different scale, set up complete feature database and can provide more fully Data support for the screening of follow-up key feature and prognostic analysis.
In the present embodiment, head and neck cancer patient's CT image that described 200 example underwent operative pathology confirm is carried out characteristic quantification, extracts 520 features such as first-order statistical properties, textural characteristics, three-dimensional configuration feature, it is achieved the feature of all data is extracted.
Step S3: adopt Computer Automatic Recognition and sorting technique to complete the forecast analysis of head and neck cancer patient. based on the clinical pathology of image feature collection all in the database completed and patient, clinical stages, transgenation and follow up a case by regular visits to the parameters such as survival time, head and neck cancer data dissimilar in database are carried out " training dataset " and the classification of " test data set ", training data is concentrated different classes of pathology data adopt computer automatic identification method to carry out model training respectively, and the training model that complete is used for the analysis and prediction that test data set realizes unknown pathology, obtain the pathology of patient, clinical stages, qualitative and the quantitative analytical results of gene information and lifetime.
In the present embodiment, finding when the head and neck cancer patient's CT image confirmed by described 200 example underwent operative pathology is analyzed, textural characteristics can divide the pathology etc. of head and neck cancer in right area. For reacting that lesion locations gray scale is uneven and when the feature such as pathology phenotype heterogeneity carries out quantitative analysis, adopt the feature such as the distance of swimming and gray level co-occurrence matrixes, the prognosis of the head and neck cancer data used reaches 82% with actual identical degree, and the CT image of head and neck cancer patient can be carried out effective quantitatively auxiliary prediction by the prognosis scheme representing proposed by the invention.
Above-described specific embodiment; the object of the present invention, technical scheme and useful effect have been further described; it it should be understood that; the foregoing is only specific embodiments of the invention; it is not limited to the present invention; within the spirit and principles in the present invention all, any amendment of making, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. the auxiliary Forecasting Methodology of the pathological tissues based on image group, it is characterised in that, comprise the following steps:
Step S101, from, patient's image database of big data quantity, adopting the dividing method of automatic or manual to extract the image data of diseased region;
Step S102, according to the segmentation result of described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, and the feature completing all diseased region image data in described patient's image database is extracted;
Step S103, based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate in described test data and realize checking.
2. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterised in that, described image data are the image data that CT, PET, mr or ultrasonograph equipment collect.
3. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterised in that, described diseased region comprises lung, liver or renal tissue.
4. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterized in that, in step S101, described automatic dividing method is by the computer implemented method based on region growing, based on the dividing method of level collection or the dividing method that cuts based on figure.
5. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterized in that, in step S102, described image feature comprises: the swollen block gray feature of the shape feature of diseased region, the textural characteristics of diseased region and/or diseased region.
6. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterised in that, described in step S103, the step of the classification that data in patient's image database carry out training dataset and test data set is comprised:
Adopt computer automatic sorting recognition methods, in conjunction with knowledge of statistics and instrument, set up and analyze the statistics correlation models of image feature and patient clinical information.
7. the auxiliary Forecasting Methodology of the pathological tissues based on image group according to claim 1, it is characterized in that, computer automatic identification method is adopted to carry out the pathological analysis of diseased region at described training dataset described in step S103, clinical stages, is analyzed, in the step of the prediction of transgenation prediction and survival time, described computer automatic identification method needs data comprise to be processed: the clinical information of described patient, biopsy result, gene information and survival time, patient clinical data, pathology hypotype residing for patient, clinical stages and TNM result by stages, gene mutation type and/or follow up a case by regular visits to survival time.
8. the pathological tissues based on image group assists pre-examining system, it is characterised in that, comprising:
From, patient's image database of big data quantity, adopting the unit of the image data of the dividing method extraction diseased region of automatic or manual;
Segmentation result according to described diseased region image, extracts the image phenotypic characteristic of each diseased region respectively, completes the unit that the feature of all diseased region image data in described patient's image database is extracted;
Based on characteristic and the clinical information data of each diseased region, data in described patient's image database are carried out the classification of training dataset and test data set, adopt computer automatic identification method to carry out the prediction of the pathological analysis of diseased region, clinical stages analysis, transgenation prediction and survival time at described training dataset, and concentrate the unit realizing checking in described test data.
9. the pathological tissues based on image group according to claim 8 assists pre-examining system, it is characterised in that, described image data are the image data that CT, PET, mr or ultrasonograph equipment collect.
10. the pathological tissues based on image group according to claim 8 assists pre-examining system, it is characterized in that, described automatic dividing method is by the computer implemented method based on region growing, based on the dividing method of level collection or the dividing method that cuts based on figure; Described image feature comprises: the swollen block gray feature of the shape feature of diseased region, the textural characteristics of diseased region and/or diseased region.
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