CN109447987A - Ulcerative colitis assistant diagnosis system and method under colonoscopy based on deep learning - Google Patents
Ulcerative colitis assistant diagnosis system and method under colonoscopy based on deep learning Download PDFInfo
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Abstract
The invention discloses ulcerative colitis assistant diagnosis system and method under a kind of colonoscopy based on deep learning, system includes colonoscopy image automatic collection subsystem, client, server-side and database；Colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；Client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to server-side, judges whether qualified, picture includes that ulcerative colitis differentiates to image；And receive and show the analysis result of server-side feedback.Database, for saving the sample set of training convolutional neural networks, the information for protecting colonoscopy image, analysis output of acquisition.The present invention utilizes image recognition technology, real-time monitoring scope video, automatic collection includes the image of emphasis organ sites and suspicious lesions region, Automatic Image Screening is carried out to image using neural network model, most worthy image can be extracted from global video, provide more reliable, efficient support for diagnosis.
Description
Technical field
The invention belongs to image identification technical field, it is related to a kind of medical endoscope image recognition system and method, specifically
It is related to ulcerative colitis assistant diagnosis system and method under a kind of colonoscopy based on deep learning.
Background technique
With deep learning algorithm continue to develop, it is increasingly mature, gradually be used for medical imaging analysis field.Endoscope
Image is the important evidence that doctor analyzes patient's disease of digestive tract, has developed a variety of utilization depth convolutional neural networks in recent years
Model is clinically of great significance to the screening of lesion, diagnostic method in current related colonoscopy diagnostic system.
Ulcerative colitis is colonic mucosa layer and submucosa continuity inflammation, is showed in continuity, more under colonoscopy
Unrestrained property distribution, mucous membrane surface is congested rotten to the corn, mostly since rectum, gradually involves total colectomy.Ulcerative colitis and intestinal tuberculosis lack
The diseases such as hemorrhagic colitis, acidophic cell enteritis, lymthoma are closely similar in Endoscopic Features, thus give ulcerative colitis
Diagnosis bring very big difficulty.In addition, judgement of the clinician in endoscopic technic according to oneself experience to suspicious lesions region,
And image-capture is got off to be saved in scope reporting system, then provide diagnosis report according to these images grabbed by diagnostician
It accuses.Enteroscopy moves back the sem observation time usually only 6~7 minutes, is limited by the working condition of operation doctor, experience influences, appearance
Easily occur emphasis diseased region missing situation, this, which will lead to clinician, can not make comprehensive and accurate assessment to lesion.
Presently disclosed correlation colonoscopy auxiliary system and method are only focused on and how to be identified via the figure of operation doctor's acquisition
As in, if comprising lesion and how to improve precision to individual target ulcerative colitis image recognition, fail to consider from
It is dynamic to acquire most worthy image and prevent from checking blind spot.
Summary of the invention
The present invention mainly solves traditional colonoscopy reporting system dependence and manually adopts figure, be easy to appear position check blind spot with can
The problem of doubtful focal area is missed, using image recognition technology, real-time monitoring scope video, automatic collection includes emphasis organ portion
Position and suspicious lesions region image, and according to the weighting algorithm overall situation preferentially after, be saved in colonoscopy reporting system.
Technical solution used by system of the invention is: ulcerative colitis is auxiliary under a kind of colonoscopy based on deep learning
Help diagnostic system, it is characterised in that: including colonoscopy image automatic collection subsystem, client, server-side and database；
The colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
The client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to the server-side,
Judge whether qualified and picture includes that ulcerative colitis differentiates to image；And receive and show point of the server-side feedback
Analyse result.
The database, for saving the sample set of training convolutional neural networks, saving colonoscopy image automatic collection subsystem
The information united for acquiring colonoscopy image, saving the acquisition report output module output.
Technical solution used by method of the invention is: a kind of intestinal tuberculosis aided diagnosis method based on deep learning,
Characterized by comprising the following steps:
Step 1: image pre-processing module receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, to image
It is pre-processed, is then delivered to convolutional neural networks module and is identified；
Step 2: convolutional neural networks module successively calls whether image qualification discrimination model, picture include exedens knot
Enteritis discrimination model judges whether qualified and picture includes that ulcerative colitis differentiates to pretreated image；And it will know
Other result returns to image display module；
Step 3: the recognition result that image display module receiving step 2 returns, and be shown on image display module；
Step 4: acquisition report output module records the differentiation each time of the convolutional neural networks module as a result, working as
After this enteroscopy is completed, sort according to confidence level and picture quality, output appendix opening, ileocaecal sphineter position image and
The N images comprising suspicious ulcerative colitis.
Manually adopt figure the present invention has the advantage that solving traditional colonoscopy system and relying on, be easy to appear Image Acquisition it is incomplete or
Person's image obscure it is unqualified, using neural network model to image carry out Automatic Image Screening after, can be mentioned from global video
Most worthy image is taken, provides more reliable, efficient support for diagnosis.
Detailed description of the invention
Attached drawing 1 is the system construction drawing of the embodiment of the present invention.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Ulcerative colitis can occur at any age, its disease incidence persistently increases in recent years, endoscopic to show as
Colonic mucosa layer and submucosa continuity inflammation are distributed in continuity, diffusivity, and mucous membrane surface is congested rotten to the corn, mostly from rectum
Start, gradually involves total colectomy.Ulcerative colitis and intestinal tuberculosis, ischemic colitis, acidophic cell enteritis, lymthoma etc.
Disease is closely similar in Endoscopic Features, thus brings very big difficulty to the diagnosis of ulcerative colitis.Improve ulcerative colitis
The key of inflammation discovery ratio is the large area generaI investigation of colonoscopy, but on the one hand the pain of traditional enteroscopy be many people hope and
Raw fear, on the other hand when doing enteroscopy, for the continuous image that equipment is passed back, doctor will directly filter out lesion figure
Piece forms report.But so more images, doctor is screened out from it lesion picture and is sought after experience, and experienced doctor
What life was a lack of again.
It is only used for collecting and managing image in relation to colonoscopy diagnostic system and application at present, needs doctor to carry out image artificial
Identify, great dependence is suffered to experience, the state of doctor, objectively constrains and image data is made full use of.And
One veteran Physician training of endoscopic technic takes a long time, and also bears classification diagnosis and treatment political affairs to basic medical unit
Disease first visit under plan guidance all causes certain pressure.
Referring to Fig.1, ulcerative colitis auxiliary diagnosis under a kind of colonoscopy based on deep learning provided by the invention, including
Colonoscopy image automatic collection subsystem, client, server-side and database；
Colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
Client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to server-side, judges image
Whether whether qualified and picture includes that ulcerative colitis differentiates；And receive and show the analysis result of server-side feedback.
Database is used for saving the sample set of training convolutional neural networks, saving colonoscopy image automatic collection subsystem
In acquisition colonoscopy image, save the information for acquiring report output module and exporting.
The client of the present embodiment includes image pre-processing module, image display module, acquisition report output module；Image
Preprocessing module pre-processes image, then for receiving the colonoscopy image of colonoscopy image automatic collection subsystem acquisition
Server-side is uploaded to be differentiated；Image display module, for receiving the differentiation result of server-side and showing；Acquire report output
Module, for recording the differentiation each time of server-side as a result, after this enteroscopy is completed, according to confidence level and picture matter
Amount sequence, N images comprising suspicious ulcerative colitis of output.
The sample set for training convolutional neural networks of the present embodiment, the acceptance or rejection picture including doctor's mark
Collection, pathological diagnosis is great to the diagnostic significance of ulcerative colitis, doctor when carrying out tissue biopsy to suspicious position often
It is influenced by materials range and depth.The normal, ulcerative colitis of doctor's mark easily with Crohn disease, intestinal tuberculosis, ischemic
Property enteritis, lymthoma, acidophic cell enteritis etc. are obscured.
The server-side of the present embodiment includes convolutional neural networks module, and whether image locations discrimination model, picture include routed
Ulcer colitis discrimination model；It is respectively used to judge whether qualified, picture includes ulcerative colitis to pretreated image
Inflammation differentiates.
The convolutional neural networks module of the present embodiment includes multiple convolutional neural networks models, uses different sample sets, instruction
Practise different model instances；
Image qualification discrimination model example: one image of input exports the probability of the picture acceptance or rejection；
Whether picture includes ulcerative colitis discrimination model example: input one image, export the picture whether include
Ulcerative colitis and probability；
VGG-16, Resnet-50, DenseNet. may be selected in convolutional neural networks model, is developed using Python,
It is called after being packaged into RESTful API (network interface of REST style) by other modules.
A kind of intestinal tuberculosis aided diagnosis method based on deep learning provided by the invention, comprising the following steps:
Step 1: image pre-processing module receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, to image
It is pre-processed and (carries out picture to cut into particular size (the present embodiment 240*240)), then the speed hair per second by 1 frame
Convolutional neural networks module is sent to be identified；
Step 2: convolutional neural networks module successively calls whether image qualification discrimination model, picture include exedens knot
Enteritis discrimination model judges whether qualified, picture includes that ulcerative colitis differentiates to pretreated image；And it will identification
As a result image display module is returned to；
Step 3: the recognition result that image display module receiving step 2 returns, and be shown on image display module；
Step 4: acquisition report output module records the differentiation each time of convolutional neural networks module as a result, working as this
After enteroscopy is completed, sort according to confidence level and picture quality, N images comprising suspicious ulcerative colitis of output.
The present invention solves traditional colonoscopy reporting system and relies on manual identified, be easy to appear image blind spot and lesion fail to pinpoint a disease in diagnosis it is disconnected
Problem detects image using neural network model, while intelligent recognition includes lesions position, carries out active prompt, shape
At the colonoscopy image diagnostic system easy to use based on artificial intelligence.With significant society and economic value.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention
Benefit requires to make replacement or deformation under protected ambit, fall within the scope of protection of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (7)
1. ulcerative colitis assistant diagnosis system under a kind of colonoscopy based on deep learning, it is characterised in that: including colonoscopy figure
As automatic collection subsystem, client, server-side and database；
The colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
The client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to the server-side, judges
Whether whether qualified and picture includes that ulcerative colitis differentiates to image；And receive and show the analysis knot of the server-side feedback
Fruit；
The database is used for saving the sample set of training convolutional neural networks, saving colonoscopy image automatic collection subsystem
In acquisition colonoscopy image, save the information for acquiring the output of report output module.
2. ulcerative colitis assistant diagnosis system, feature under the colonoscopy according to claim 1 based on deep learning
Be: the client includes image pre-processing module, image display module, acquisition report output module；
Described image preprocessing module, for receiving the colonoscopy image of the colonoscopy image automatic collection subsystem acquisition, to figure
As being pre-processed, then uploads to server-side and differentiated；
Described image display module, for receiving the differentiation result of the server-side and showing；
The acquisition report output module, for recording the differentiation each time of the server-side as a result, when this enteroscopy is complete
At later, sort according to confidence level and picture quality, N images comprising suspicious ulcerative colitis of output.
3. ulcerative colitis assistant diagnosis system, feature under the colonoscopy according to claim 1 based on deep learning
Be: the server-side includes convolutional neural networks module, including whether image qualification discrimination model, picture include exedens
Colitis discrimination model；It is respectively used to judge whether pretreated image whether sentence comprising ulcerative colitis by qualified, picture
Not.
4. ulcerative colitis assistant diagnosis system, feature under the colonoscopy according to claim 3 based on deep learning
It is: described image qualification discrimination model, for exporting the general of the picture acceptance or rejection by one image of input
Rate.
5. ulcerative colitis assistant diagnosis system, feature under the colonoscopy according to claim 3 based on deep learning
Be: whether the picture includes ulcerative colitis discrimination model, for whether exporting the picture by one image of input
Include ulcerative colitis and probability.
6. ulcerative colitis aided diagnosis method under a kind of colonoscopy based on deep learning, which is characterized in that including following step
It is rapid:
Step 1: image pre-processing module receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, carries out to image
Pretreatment, is then delivered to convolutional neural networks module and is identified；
Step 2: convolutional neural networks module successively calls whether image qualification discrimination model, picture include ulcerative colitis
Discrimination model judges whether qualified, picture includes that ulcerative colitis differentiates to pretreated image；And by recognition result
Return to image display module；
Step 3: the recognition result that image display module receiving step 2 returns, and be shown on image display module；
Step 4: acquisition report output module records the differentiation each time of the convolutional neural networks module as a result, when this
After enteroscopy is completed, sort according to confidence level and picture quality, output appendix opening, the image at ileocaecal sphineter position and N
Image comprising suspicious ulcerative colitis.
7. the intestinal tuberculosis aided diagnosis method according to claim 6 based on deep learning, it is characterised in that: in step 1,
It is described that image is pre-processed, it is to carry out picture to cut into particular size, is then sent to convolution by 1 frame speed per second
Neural network module is identified.
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| CN110020610A (en) * | 2019-03-16 | 2019-07-16 | 复旦大学 | Colonoscopy quality examination control system based on deep learning |
| CN110175983A (en) * | 2019-04-17 | 2019-08-27 | 平安科技（深圳）有限公司 | Eyeground lesion screening method, device, computer equipment and storage medium |
| CN110993099A (en) * | 2019-12-18 | 2020-04-10 | 山东大学齐鲁医院 | Ulcerative colitis severity evaluation method and system based on deep learning |
| CN111091559A (en) * | 2019-12-17 | 2020-05-01 | 山东大学齐鲁医院 | Depth learning-based auxiliary diagnosis system for small intestine sub-scope lymphoma |
| CN111524124A (en) * | 2020-04-27 | 2020-08-11 | 中国人民解放军陆军特色医学中心 | Digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease |
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| CN110020610A (en) * | 2019-03-16 | 2019-07-16 | 复旦大学 | Colonoscopy quality examination control system based on deep learning |
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| CN111091559A (en) * | 2019-12-17 | 2020-05-01 | 山东大学齐鲁医院 | Depth learning-based auxiliary diagnosis system for small intestine sub-scope lymphoma |
| CN110993099B (en) * | 2019-12-18 | 2020-11-03 | 山东大学齐鲁医院 | Ulcerative colitis severity evaluation method and system based on deep learning |
| CN110993099A (en) * | 2019-12-18 | 2020-04-10 | 山东大学齐鲁医院 | Ulcerative colitis severity evaluation method and system based on deep learning |
| CN111524124A (en) * | 2020-04-27 | 2020-08-11 | 中国人民解放军陆军特色医学中心 | Digestive endoscopy image artificial intelligence auxiliary system for inflammatory bowel disease |
| CN112201335A (en) * | 2020-07-23 | 2021-01-08 | 中国人民解放军总医院 | System and method for identifying structure in abdominal cavity under linear array ultrasonic endoscope |
| CN112215835A (en) * | 2020-10-22 | 2021-01-12 | 刘茗露 | Information processing method and device for template report in image-text system |
| CN113012131A (en) * | 2021-03-19 | 2021-06-22 | 重庆金山医疗器械有限公司 | Endoscope image processing method, device, equipment and medium |
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Application publication date: 20190308 |