CN109615633A - Crohn disease assistant diagnosis system and method under a kind of colonoscopy based on deep learning - Google Patents
Crohn disease assistant diagnosis system and method under a kind of colonoscopy based on deep learning Download PDFInfo
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Abstract
The invention discloses Crohn disease assistant diagnosis system and method under a kind of colonoscopy based on deep learning, system includes colonoscopy image automatic collection subsystem, database, client and server-side；Colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；Database includes the sample set for training convolutional neural networks；Client is used to for the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to server-side, and receives and show the analysis result of server-side feedback.The present invention utilize image recognition technology, real-time monitoring scope video, automatic collection include emphasis organ sites and suspicious Crohn disease stove region image, and according to the weighting algorithm overall situation preferentially after, provide whether be Crohn disease diagnosis.After the present invention carries out Automatic Image Screening to image using neural network model, most worthy image can be extracted from global video, and provide auxiliary diagnosis, 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 Crohn disease 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.
Crohn disease is noncontinuity holostrome inflammation, and performance is in segmental or jumping characteristic under colonoscopy, without being in continuity；
Stringer or crack ulcer can be formed；Lesion involves intestinal wall holostrome, and intestinal wall, which thickens, to be hardened.Crohn disease and intestinal tuberculosis, ischemic
The diseases such as colitis, acidophic cell enteritis, lymthoma are closely similar in Endoscopic Features, thus give the diagnosis band of Crohn disease
Carry out very big difficulty.In addition, judgement of the clinician in endoscopic technic according to oneself experience to suspicious lesions region, and by image
It grabs and is saved in scope reporting system, then provide diagnosis report according to these images grabbed by diagnostician.Colonoscopy
Inspection moves back the sem observation time usually only 6~7 minutes, is limited by the operation working condition of doctor, experience influences, be easy to appear by
Emphasis diseased region misses 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 Crohn disease image recognition, fail to consider to adopt automatically
Collect most worthy image and prevents 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 Crohn disease stove region image, and according to the weighting algorithm overall situation preferentially after, be saved in colonoscopy reporting system,
Simultaneously provide whether be Crohn disease diagnosis.
Technical solution used by system of the invention is: Crohn disease auxiliary is examined under a kind of colonoscopy based on deep learning
Disconnected system, it is characterised in that: including colonoscopy image automatic collection subsystem, database, client and server-side；
The colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
The database includes the sample set for training convolutional neural networks, including doctor mark qualified pictures and
Unqualified pictures；Wherein qualified pictures include normal bowel pictures, ileocaecal sphineter and the appendix opening figure of position identification again
The pictures and other intestines problem lesion pictures of piece collection, Crohn disease lesion；
The client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to the server-side,
And receive and show the analysis result of the server-side feedback.
Technical solution used by method of the invention is: Crohn disease auxiliary is examined under a kind of colonoscopy based on deep learning
Disconnected method, which comprises the following steps:
Step 1: client receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, then uploads to service
End；
Step 2: server-side receives the picture that client transmits and activates video reception module as solicited message, enables volume
Product neural network module successively calls image qualification discrimination model, Crohn disease stove discrimination model, whether qualified carries out image
Differentiate, image locations differentiate and whether picture includes that Crohn disease stove differentiates；And it exports result and gives colonoscopy reporting modules；
Step 3: colonoscopy reporting modules record the differentiation each time of convolutional neural networks module as a result, working as this colonoscopy
It after having checked, sorts according to confidence level and picture quality, output appendix opening, the image at ileocaecal sphineter position and N include
The image of suspicious lesions；And export whether be Crohn disease diagnosis, by above-mentioned report result be sent to client image show
Module；
Step 4: the recognition result that figure display module receiving step 3 returns, and be shown on graphical interfaces, it shows simultaneously
Related text information.
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, and provides auxiliary diagnosis, 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.
Crohn disease can occur at any age, and men and women's illness rate is approximate, and its disease incidence has and persistently increases in recent years
Gesture, it is endoscopic to show as showing under its colonoscopy in segmental or jumping characteristic, without being in continuity；Stringer can be formed or split
Gap ulcer；Lesion involves intestinal wall holostrome, and intestinal wall, which thickens, to be hardened.Crohn disease and intestinal tuberculosis, ischemic colitis, acidophic cell
The diseases such as enteritis, lymthoma are closely similar in Endoscopic Features, thus bring very big difficulty to the diagnosis of Crohn disease.Raising gram
The key of sieve grace disease discovery ratio is the large area generaI investigation of colonoscopy, but on the one hand the pain of traditional enteroscopy is many people
Daunting, on the other hand when doing enteroscopy, for the continuous image that equipment is passed back, doctor will directly filter out disease
Stove picture forms report.But so more images, doctor is screened out from it lesion picture and is sought after experience, and has experience
Doctor be again a lack of.
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, Crohn disease assistant diagnosis system under a kind of colonoscopy based on deep learning provided by the invention, including
Colonoscopy image automatic collection subsystem, database, client and server-side；
Colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
Database includes the sample set for training convolutional neural networks, including doctor mark qualified pictures and do not conform to
Lattice pictures；Wherein qualified pictures include again position identification normal bowel pictures, ileocaecal sphineter and appendix be open pictures,
The pictures of Crohn disease lesion and other intestines problem lesion pictures；
Client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquire being uploaded to server-side, and receive with
Show the analysis result of server-side feedback.
The client of the present embodiment includes communication module and image display module；Communication module is for transmiting a request to service
End, and analysis result is obtained from server-side；Image display module, for receive the differentiation of server-side as a result, and shown with
Graph text information expression: the virtual image of a colon is constructed, is grey under original state, will detect whether sliding mirror bring occur
It fails to pinpoint a disease in diagnosis, and prompts the Hui Jing that whether succeeds.
The server-side of the present embodiment includes video reception module, convolutional neural networks module, colonoscopy reporting modules；Video connects
Module is received, by video frequency collection card, connects the bnc interface of colonoscopy image automatic collection subsystem, for receiving colonoscopy image certainly
The colonoscopy image of dynamic acquisition subsystem acquisition, calls convolutional neural networks module；Convolutional neural networks module, including image are qualified
Property discrimination model, Crohn disease stove discrimination model, for differentiating the qualification of image, with the presence or absence of Crohn disease stove；Colonoscopy report
Module is accused, for recording the differentiation each time of convolutional neural networks module as a result, after this enteroscopy is completed, according to
Confidence level and picture quality weighting after is ranked up, output whether be Crohn disease diagnosis.
The image qualification discrimination model example of the present embodiment: one image of input exports the picture acceptance or rejection
Probability；
The Crohn disease stove discrimination model example of the present embodiment: one image of input exports whether the picture includes lesion
And probability；
VGG-16 or DenseNet can be selected in the model of the present embodiment, is developed using Python, is packaged into RESTful
It is called after API (network interface of REST style) by other modules.Convolutional neural networks model is normal for field of image recognition
Technological means is advised, is no longer repeated herein.
Crohn disease aided diagnosis method under a kind of colonoscopy based on deep learning provided by the invention, including following step
It is rapid:
Step 1: client receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, then uploads to service
End；
Step 2: server-side receives the picture that client transmits and activates video reception module as solicited message, enables volume
Product neural network module successively calls image qualification discrimination model, Crohn disease stove discrimination model, whether qualified carries out image
Differentiate, whether picture includes that Crohn disease stove differentiates；And it exports result and gives colonoscopy reporting modules；
Step 3: colonoscopy reporting modules record the differentiation each time of convolutional neural networks module as a result, working as this colonoscopy
After having checked, sort according to confidence level and picture quality, output whether be Crohn disease diagnosis, by above-mentioned report result
It is sent to client image display module；
Step 4: the recognition result that figure display module receiving step 3 returns, and be shown on graphical interfaces, it shows simultaneously
Related text information.
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 the significant points in intelligent recognition enteroscopy, provide whether
For the auxiliary diagnosis of Crohn disease, the colonoscopy visual aids diagnostic system easy to use based on deep learning is formd.Have
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. Crohn disease assistant diagnosis system under a kind of colonoscopy based on deep learning, it is characterised in that: certainly including colonoscopy image
Dynamic acquisition subsystem, database, client and server-side；
The colonoscopy image automatic collection subsystem is for acquiring colonoscopy image；
The database includes the sample set for training convolutional neural networks, including doctor mark qualified pictures and do not conform to
Lattice pictures；Wherein qualified pictures include again position identification normal bowel pictures, ileocaecal sphineter and appendix be open pictures,
The pictures of Crohn disease lesion and other intestines problem lesion pictures；
The client is used to the colonoscopy image that colonoscopy image automatic collection subsystem acquires being uploaded to the server-side, and connects
Receive and show the analysis result of the server-side feedback.
2. Crohn disease assistant diagnosis system under the colonoscopy according to claim 1 based on deep learning, it is characterised in that:
The client includes communication module and image display module；The communication module is for transmiting a request to server-side, and from clothes
Business end obtains analysis result；Described image display module, for receiving the differentiation of the server-side as a result, and being shown to scheme
Literary information representation.
3. Crohn disease assistant diagnosis system under the colonoscopy according to claim 2 based on deep learning, it is characterised in that:
Described image display module constructs the virtual image of a colon, is grey under original state, will detect whether sliding mirror band occur
That comes fails to pinpoint a disease in diagnosis, and prompts the Hui Jing that whether succeeds.
4. Crohn disease assistant diagnosis system under the colonoscopy according to claim 1 based on deep learning, it is characterised in that:
The server-side includes video reception module, convolutional neural networks module, colonoscopy reporting modules；
The video reception module calls volume for receiving the colonoscopy image of the colonoscopy image automatic collection subsystem acquisition
Product neural network module；
The convolutional neural networks module, including image qualification discrimination model, Crohn disease stove discrimination model, for differentiating figure
The qualification of picture whether there is Crohn disease stove；
The colonoscopy reporting modules, for recording the differentiation each time of the convolutional neural networks module as a result, when this intestines
After spectroscopy is completed, according to being ranked up after confidence level and picture quality weighting, output ileocaecal sphineter, appendix opening position
The image and N images comprising suspicious lesions, and export whether be Crohn disease diagnosis.
5. Crohn disease assistant diagnosis system under the colonoscopy according to claim 4 based on deep learning, it is characterised in that:
Described image qualification discrimination model, for exporting the probability of the picture acceptance or rejection by one image of input.
6. Crohn disease assistant diagnosis system under the colonoscopy according to claim 4 based on deep learning, it is characterised in that:
Whether the Crohn disease stove discrimination model includes Crohn disease stove and general for by one image of input, exporting the picture
Rate.
7. Crohn disease aided diagnosis method under a kind of colonoscopy based on deep learning, which comprises the following steps:
Step 1: client receives the colonoscopy image of colonoscopy image automatic collection subsystem acquisition, then uploads to server-side；
Step 2: server-side receives the picture that client transmits and activates video reception module as solicited message, enables convolution mind
Successively call image qualification discrimination model, Crohn disease stove discrimination model through network module, carry out image it is whether qualified differentiate,
Whether differentiate comprising Crohn disease stove；And it exports result and gives colonoscopy reporting modules；
Step 3: colonoscopy reporting modules record the differentiation each time of convolutional neural networks module as a result, working as this enteroscopy
After completion, sort according to confidence level and picture quality, output whether be Crohn disease diagnosis, above-mentioned report result is sent
Give client image display module；
Step 4: the recognition result that figure display module receiving step 3 returns, and be shown on graphical interfaces, while showing correlation
Text information.
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Cited By (14)
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| CN109998488A (en) * | 2019-04-13 | 2019-07-12 | 中国医学科学院北京协和医院 | The identification model and construction method of Crohn disease and enteron aisle ulcer type lymthoma |
| CN110136808A (en) * | 2019-05-23 | 2019-08-16 | 安翰科技(武汉)股份有限公司 | A kind of filming apparatus auxiliary display system |
| CN111047582A (en) * | 2019-12-17 | 2020-04-21 | 山东大学齐鲁医院 | Crohn's disease auxiliary diagnosis system under enteroscope based on degree of depth 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 |
| CN111341441A (en) * | 2020-03-02 | 2020-06-26 | 刘四花 | Gastrointestinal disease model construction method and diagnosis system |
| CN111477325A (en) * | 2020-04-09 | 2020-07-31 | 赣南师范大学 | Method for identifying intestinal ulcer disease caused based on particle swarm optimization algorithm |
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| CN111839427A (en) * | 2019-04-25 | 2020-10-30 | 天津御锦人工智能医疗科技有限公司 | Method for preventing iatrogenic intestinal wall perforation based on image recognition |
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| CN111839422A (en) * | 2019-04-25 | 2020-10-30 | 天津御锦人工智能医疗科技有限公司 | Tumor-like lesion recognition workstation 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 |
| CN111047582A (en) * | 2019-12-17 | 2020-04-21 | 山东大学齐鲁医院 | Crohn's disease auxiliary diagnosis system under enteroscope based on degree of depth learning |
| CN111341441A (en) * | 2020-03-02 | 2020-06-26 | 刘四花 | Gastrointestinal disease model construction method and diagnosis system |
| CN111477325A (en) * | 2020-04-09 | 2020-07-31 | 赣南师范大学 | Method for identifying intestinal ulcer disease caused based on particle swarm optimization algorithm |
| CN112215835A (en) * | 2020-10-22 | 2021-01-12 | 刘茗露 | Information processing method and device for template report in image-text system |
| CN115132355A (en) * | 2022-07-13 | 2022-09-30 | 山东大学齐鲁医院 | Intelligent data auxiliary diagnosis system for inflammatory bowel disease |
| CN115132355B (en) * | 2022-07-13 | 2024-05-03 | 山东大学齐鲁医院 | Intelligent data auxiliary diagnosis system for inflammatory bowel disease |
| CN115578437A (en) * | 2022-12-01 | 2023-01-06 | 武汉楚精灵医疗科技有限公司 | Intestinal body focus depth data acquisition method and device, electronic equipment and storage medium |
| CN115578437B (en) * | 2022-12-01 | 2023-03-14 | 武汉楚精灵医疗科技有限公司 | Intestinal body focus depth data acquisition method and device, electronic equipment and storage medium |
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Application publication date: 20190412 |