CN111091562A - Method and system for measuring size of digestive tract lesion - Google Patents
Method and system for measuring size of digestive tract lesion Download PDFInfo
- Publication number
- CN111091562A CN111091562A CN201911338548.6A CN201911338548A CN111091562A CN 111091562 A CN111091562 A CN 111091562A CN 201911338548 A CN201911338548 A CN 201911338548A CN 111091562 A CN111091562 A CN 111091562A
- Authority
- CN
- China
- Prior art keywords
- reference object
- size
- image
- lesion
- digestive tract
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10068—Endoscopic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30028—Colon; Small intestine
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30092—Stomach; Gastric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30096—Tumor; Lesion
Abstract
The invention discloses a method and a system for measuring the size of a digestive tract lesion, wherein the method comprises the following steps: acquiring an endoscopic image of the digestive tract containing a lesion and a reference object; identifying a reference contained therein; calculating the width of the intersection of the reference object and the alimentary canal mucosa; combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size; extracting a lesion region contained in an endoscopic image of a digestive tract; and obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size. The invention can realize the accurate measurement of the size of the focus without additional equipment and prolonged operation time in the endoscope operation process.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a method and a system for measuring the size of a digestive tract lesion.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The use of electronic endoscopes for diagnosis makes it difficult to accurately measure the size of the lesion before taking out the biopsy of the lesion, and the measurement of the size of the lesion under the endoscope is related to the decision-making and data analysis of the endoscope treatment. Due to the lack of sensors, endoscopes cannot conveniently measure polyp size as ultrasound does. In clinical practice, most endoscopists estimate polyp size by visual inspection or open biopsy forceps. However, studies have demonstrated that endoscopists often cannot accurately estimate polyp size outside the body, which directly impacts the choice of treatment decision. At present, a plurality of methods are used for measuring pathological changes, and according to the knowledge of the inventor, the established method conveys reference objects into the body, such as specially-made measuring reference objects, laser beams projected by an external laser device and the like; or a space coordinate system is established by purchasing a special three-dimensional endoscope, the size of a lesion is obtained by a three-dimensional reconstruction technology, and the like, but the methods more or less require additional equipment or endoscope operation time, and the measurement accuracy needs to be improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for measuring the size of a digestive tract focus, which can realize the accurate measurement of the size of the focus without additional equipment and prolonged operation time in the endoscope operation process.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a method for measuring the size of a digestive tract lesion comprises the following steps:
acquiring an endoscopic image of the digestive tract containing a lesion and a reference object;
identifying a reference contained therein;
calculating the width of the intersection of the reference object and the alimentary canal mucosa;
combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
extracting a lesion region contained in an endoscopic image of a digestive tract;
and obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
Furthermore, a reference object contained in the identification image adopts a pre-constructed reference object identification model; the model construction method comprises the following steps:
acquiring training data, wherein the training data is a digestive tract endoscope sample image with a reference object outline labeled in advance;
performing mask processing on sample images in the training data;
and training a Mask R-CNN model by adopting the training data subjected to Mask processing to obtain a reference object recognition model.
Further, calculating the width of intersection of the reference object with the alimentary tract mucosa comprises:
carrying out corner point detection on the identified outline of the reference object;
and calculating the intersection width of the reference object and the alimentary canal mucosa according to the detected corner points and the reference object outline.
Further, calculating the width of intersection of the reference object with the alimentary tract mucosa comprises:
identifying an angular point on one side of the junction of the reference object and the alimentary tract mucosa according to the contour of the reference object;
taking a plurality of points on the profile line of the opposite side, and performing straight line fitting based on the plurality of points to obtain a fitted straight line of the opposite side;
and projecting the identified angular points onto a fitting straight line, wherein the distance between the obtained projection points and the angular points is the intersection width of the reference object and the digestive tract mucosa.
Further, an edge extraction algorithm is adopted for extracting a lesion region contained in the image.
Further, obtaining the actual size of the lesion comprises:
extracting an external rectangle of the focus area, and acquiring the number of pixels corresponding to the length and the width of the external rectangle;
and obtaining the actual size of the length and the width of the circumscribed rectangle of the focus according to the corresponding relation between the image pixels and the actual size.
Further, the reference object is a jet forward water column or a biopsy forceps.
One or more embodiments provide a digestive tract lesion size measuring system including:
a reference object image acquisition module for acquiring an endoscope image of the digestive tract containing a lesion and a reference object;
a reference object identification module for identifying a reference object contained therein;
the pixel and actual size corresponding relation calculation module is used for calculating the size of the intersection of the reference object and the alimentary canal mucosa; combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
a focus extraction module for extracting a focus region contained in the digestive tract endoscope image;
and the focus size calculation module is used for obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
One or more embodiments provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of digestive tract lesion size measurement when executing the program.
One or more embodiments provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the digestive tract lesion size measuring method.
The above one or more technical solutions have the following beneficial effects:
the method for measuring the size of the focus provided by the invention adopts the forward water column or the biopsy forceps in the endoscope as a lesion measurement reference standard, utilizes the convolutional neural network to identify the size of the junction of the forward water column or the biopsy forceps and the digestive tract mucosa as a measurement scale, and calculates the size of the focus according to the diameter of a lesion projection surface of a reference object in an image.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow diagram of a method for measuring a size of a digestive tract lesion in accordance with one or more embodiments of the present invention;
FIG. 2a is a gastroscopic image of a biopsy forceps-containing tool according to one or more embodiments of the present invention;
FIG. 2b is a gastroscopic image containing a forward water column according to one or more embodiments of the present invention;
FIG. 3 is a graph illustrating the effect of identifying a forward water column in one or more embodiments of the present invention;
FIG. 4 is a profile image of a reference object generated in one or more embodiments of the invention;
FIG. 5 is a schematic view of the interface of a reference object and the gastric mucosa in accordance with one or more embodiments of the present invention;
FIG. 6 is a gastroscopic image containing a lesion taken in one or more embodiments of the present invention;
FIG. 7 is a schematic view of a circumscribed rectangle of a lesion area in accordance with one or more embodiments of the present invention;
FIG. 8 is a schematic view of a sized lesion region in accordance with one or more embodiments of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and further it is to be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiments and features of the embodiments of the invention may be combined with each other without conflict.
Example one
As shown in fig. 1, the present embodiment discloses a method for measuring the size of a digestive tract lesion, taking the measurement of the size of a lesion on a gastric mucosa as an example, the method includes the following steps:
step 1: during an endoscopic procedure, a gastroscopic image containing the lesion and a reference is acquired.
Because the distance between the lens of the gastroscope and the gastric mucosa can change during the gastroscope examination, in order to ensure that the pathological changes and the reference objects are far and near the same lens, when a doctor finds the pathological changes, the gastroscope image containing the pathological changes and the reference objects is collected. The reference may be a forward jet of water or a biopsy forceps. As the water column and the biopsy forceps belong to auxiliary means or tools frequently used by doctors in the gastroscopy process, the water column and the biopsy forceps are used as reference objects, and the gastroscopy image containing the pathological changes and the reference objects simultaneously is obtained, so that the gastroscopy process is not obviously prolonged, and the gastroscopy process can be finished in the normal examination process.
The examination condition of the patient is reflected on the display screen by adopting the electronic gastroscope, and the gastroscope image with the measurement standard reference object is stored as data, so that the target gastroscope image to be detected is obtained.
The distortion of the lens of the electronic gastroscope can cause errors on the focus measured under the gastroscope, so the distortion of the lens of the electronic gastroscope needs to be corrected.
Step 2: and detecting the gastroscope image acquired by the electronic gastroscope based on the trained reference object identification model of the gastroscope image to acquire the outline of the labeled reference object, as shown in fig. 3-4.
Wherein, the contour detection model for training the gastroscope image reference object specifically comprises:
(1) and acquiring a sample gastroscope image containing a reference object, and carrying out contour labeling on the reference object in the sample gastroscope image to obtain a training sample.
(2) And carrying out image processing on the sample gastroscope image to obtain a sample gastroscope image data set.
Wherein the image processing comprises: mask image generation, bicubic interpolation scaling process of 4x4 pixel neighborhood. The image after the reduction is processed by the mask can keep the detail characteristics of the image as much as possible, which is beneficial to the training of the network.
(3) And training a MASK-RCNN convolution neural network according to the sample gastroscope image data set to obtain a trained reference object identification model for identifying the gastroscope image.
Dividing a sample gastroscope image data set into a sample gastroscope image training set, a sample gastroscope image testing set and a sample gastroscope image verification set according to the ratio of 6:2:2, and inputting the sample gastroscope image training set, the sample gastroscope image testing set and the sample gastroscope image verification set into a reference object identification model of a gastroscope image to be trained. In the embodiment of the invention, the reference object recognition model is obtained based on convolutional neural network training, the convolutional neural network comprises a plurality of convolutional layers, and a plurality of optional convolutional cores are superposed in each convolutional layer so as to improve the performance of the convolutional neural network. After a series of convolution and pooling operations of the convolution neural network are carried out on the sample gastroscope image data set, image characteristics of the sample gastroscope image are extracted, whether the image of the sample gastroscope image contains a reference object to be detected such as a water column or a biopsy forceps is judged according to the image characteristics, the convolution neural network is tested and verified through the sample gastroscope image test set and the sample gastroscope image verification set, and if preset conditions are met, a trained reference object identification model is obtained.
And step 3: calculating the width of intersection of the reference object with the mucosa of the digestive tract.
Wherein, for the water column, the crossing width of the water column and the alimentary canal mucosa is the diameter of the contact surface of the water column sprayed to the alimentary canal mucosa.
3.1: firstly, a corner set with the maximum characteristic value is searched in a contour image, and the corner set is realized by adopting a Shi-Tomasi corner detection algorithm.
3.2: obtaining the corner points at two ends of the junction of the reference object and the alimentary canal mucosa. Because the angle of the water column jet is fixed, the right angular point can be accurately identified, and the left angular point is not easy to accurately identify, the right angular point at the junction of the reference object and the mucosa is identified first, then 5 points are taken from the left side of the contour of the reference object, a least binary fitting linear algorithm is adopted for the 5 points, a linear equation (y is kx + d) of the left contour line is fitted, a projection point of the right angular point on the left contour line is solved, the right angular point and the projection point are regarded as angular points at two ends of the junction of the reference object such as the water column or the biopsy forceps and the mucosa of the alimentary tract, and the distance between the two points is calculated to be the width of the intersection between the reference object and the mucosa of the alimentary tract, as shown in fig. 5.
And combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size. For example, according to the real experiment, the diameter of the contact surface between the reference object such as the water column and the mucosa of the digestive tract is 1mm, and the actual diameter of the two contact points determined according to the step 4 is considered to be 1 mm.
And 4, step 4: a lesion region is extracted from an endoscopic image of the digestive tract.
The acquired gastroscope image containing the lesion is shown in fig. 6, and the contour image of the lesion is acquired according to a Canny boundary extraction algorithm, and the extracted contour of the lesion is shown in fig. 7. The size of the rectangular area can be obtained according to the position of the outline area.
And 5: comparing the diameters of the reference objects to obtain the size of the lesion in the image.
According to the following formula: the distance between two points at the junction/the length of the focus area in the image, and the width distance are equal to the actual length of the junction/the actual length of the focus area.
Step 6: according to the corresponding relationship between the image pixels and the actual size, the actual size of the lesion is obtained, as shown in fig. 8.
Example two
The purpose of this embodiment is to provide a digestive tract focus size measurement system.
A digestive tract lesion size measurement system comprising:
a reference object image acquisition module for acquiring an endoscope image of the digestive tract containing a lesion and a reference object;
a reference object identification module for identifying a reference object contained therein;
the pixel and actual size corresponding relation calculation module is used for calculating the size of the intersection of the reference object and the alimentary canal mucosa; combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
a focus extraction module for extracting a focus region contained in the digestive tract endoscope image;
and the focus size calculation module is used for obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
EXAMPLE III
The embodiment aims at providing an electronic device.
An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program, comprising:
acquiring an endoscopic image of the digestive tract containing a lesion and a reference object;
identifying a reference contained therein;
calculating the size of the junction of the reference object and the alimentary canal mucosa;
combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
extracting a lesion region contained in an endoscopic image of a digestive tract;
and extracting the focus area contained in the image, and obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
Example four
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs the steps of:
acquiring an endoscopic image of the digestive tract containing a lesion and a reference object;
identifying a reference contained therein;
calculating the size of the junction of the reference object and the alimentary canal mucosa;
combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
extracting a lesion region contained in an endoscopic image of a digestive tract;
and obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
The steps involved in the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
One or more of the above embodiments have the following technical effects:
the method for measuring the size of the focus provided by the invention adopts the forward water column or the biopsy forceps in the endoscope as a lesion measurement reference standard, adopts the convolutional neural network to identify the size of the junction of the forward water column or the biopsy forceps and the digestive tract mucosa as a measurement scale, and calculates the size of the focus according to the thickness change of a reference object in an image.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented using general purpose computing apparatus, or alternatively, they may be implemented using program code executable by computing apparatus, whereby the modules or steps may be stored in a memory device and executed by computing apparatus, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for measuring the size of a digestive tract focus is characterized by comprising the following steps:
acquiring an endoscopic image of the digestive tract containing a lesion and a reference object;
identifying a reference contained therein;
calculating the width of the intersection of the reference object and the alimentary canal mucosa;
combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
extracting a lesion region contained in an endoscopic image of a digestive tract;
and obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
2. The method for measuring the size of a digestive tract lesion according to claim 1, wherein the reference object included in the identification image is a reference object identification model constructed in advance; the model construction method comprises the following steps:
acquiring training data, wherein the training data is a digestive tract endoscope sample image with a reference object outline labeled in advance;
performing mask processing on sample images in the training data;
and training a Mask R-CNN model by adopting the training data subjected to Mask processing to obtain a reference object recognition model.
3. The method of claim 1, wherein calculating the width of intersection of the reference object with the alimentary tract mucosa comprises:
carrying out corner point detection on the identified outline of the reference object;
and calculating the intersection width of the reference object and the alimentary canal mucosa according to the detected corner points and the reference object outline.
4. The method of claim 3, wherein calculating the width of intersection of the reference object with the alimentary tract mucosa comprises:
identifying an angular point on one side of the junction of the reference object and the alimentary tract mucosa according to the contour of the reference object;
taking a plurality of points on the profile line of the opposite side, and performing straight line fitting based on the plurality of points to obtain a fitted straight line of the opposite side;
and projecting the identified angular points onto a fitting straight line, wherein the distance between the obtained projection points and the angular points is the intersection width of the reference object and the alimentary tract mucosa.
5. The method of claim 1, wherein the step of extracting lesion areas uses an edge extraction algorithm.
6. The method of claim 1, wherein obtaining the actual size of the lesion comprises:
extracting an external rectangle of the focus area, and acquiring the number of pixels corresponding to the length and the width of the external rectangle;
and obtaining the actual size of the length and the width of the circumscribed rectangle of the focus according to the corresponding relation between the image pixels and the actual size.
7. The method of claim 1, wherein the reference object is a jet of forward water or a biopsy forceps.
8. A digestive tract lesion size measuring system, comprising:
a reference object image acquisition module for acquiring an endoscope image of the digestive tract containing a lesion and a reference object;
a reference object identification module for identifying a reference object contained therein;
the pixel and actual size corresponding relation calculation module is used for calculating the size of the junction of the reference object and the alimentary canal mucosa; combining the size of the junction of the reference object and the alimentary tract mucosa obtained by a real experiment to obtain the corresponding relation between the image pixel and the actual size;
a focus extraction module for extracting a focus region contained in the digestive tract endoscope image;
and the focus size calculation module is used for obtaining the actual size of the focus according to the corresponding relation between the image pixel and the actual size.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements a method of digestive tract lesion size measurement according to any of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements the digestive tract lesion size measuring method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911338548.6A CN111091562B (en) | 2019-12-23 | 2019-12-23 | Method and system for measuring size of digestive tract lesion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911338548.6A CN111091562B (en) | 2019-12-23 | 2019-12-23 | Method and system for measuring size of digestive tract lesion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111091562A true CN111091562A (en) | 2020-05-01 |
| CN111091562B CN111091562B (en) | 2020-12-01 |
Family
ID=70395892
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911338548.6A Active CN111091562B (en) | 2019-12-23 | 2019-12-23 | Method and system for measuring size of digestive tract lesion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111091562B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260648A (en) * | 2020-05-06 | 2020-06-09 | 杭州迪英加科技有限公司 | Method and device for measuring lesion area of gastric mucosa ESD excision specimen image |
| CN112153320A (en) * | 2020-09-23 | 2020-12-29 | 北京京东振世信息技术有限公司 | Method and device for measuring size of article, electronic equipment and storage medium |
| CN112257506A (en) * | 2020-09-21 | 2021-01-22 | 北京豆牛网络科技有限公司 | Fruit and vegetable size identification method and device, electronic equipment and computer readable medium |
| CN112580437A (en) * | 2020-11-26 | 2021-03-30 | 复旦大学附属中山医院 | Method and device for measuring size of target |
| CN112863699A (en) * | 2020-12-31 | 2021-05-28 | 山东大学齐鲁医院 | ESD preoperative discussion system based on mobile terminal |
| CN114052641A (en) * | 2021-11-04 | 2022-02-18 | 深圳市博盛医疗科技有限公司 | Laparoscope system for reducing three-dimensional distortion in 3D vision based on intelligent optimization technology |
| CN114740614A (en) * | 2022-05-07 | 2022-07-12 | 深圳市微视光电科技有限公司 | Endoscope probe capable of assisting in measurement |
| CN116958147A (en) * | 2023-09-21 | 2023-10-27 | 青岛美迪康数字工程有限公司 | Target area determining method, device and equipment based on depth image characteristics |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102670155A (en) * | 2012-03-19 | 2012-09-19 | 中国人民解放军南京军区南京总医院 | Method for establishing object and image correlation factor for endoscope measurement and applications thereof in endoscope measurement |
| CN103505217A (en) * | 2012-06-28 | 2014-01-15 | 吕海涛 | Method for accurately measuring size of nidus under endoscope |
| CN104083172A (en) * | 2013-11-28 | 2014-10-08 | 北京华科创智健康科技股份有限公司 | Method and device for quantitatively measuring size of focus through electronic endoscope |
| CN104146711A (en) * | 2014-08-01 | 2014-11-19 | 深圳市开立科技有限公司 | Endoscope based lesion size measurement method and system |
| CN106456271A (en) * | 2014-03-28 | 2017-02-22 | 直观外科手术操作公司 | Alignment of q3d models with 3d images |
| WO2018008195A1 (en) * | 2016-07-05 | 2018-01-11 | オリンパス株式会社 | Image processing device, image processing system, method for operating image processing device, and program for operating image processing device |
| CN109141250A (en) * | 2018-08-08 | 2019-01-04 | 深圳码隆科技有限公司 | Luggage dimension measurement method, device and luggage dimensional measurement server |
| CN109381152A (en) * | 2017-08-04 | 2019-02-26 | 卡普索影像公司 | For the area of the object of interest in stomach image or the method for volume and equipment |
| CN110200580A (en) * | 2019-06-14 | 2019-09-06 | 石焕 | A kind of householder method for tumor in digestive tract clinic early detection |
| CN110473192A (en) * | 2019-04-10 | 2019-11-19 | 腾讯医疗健康(深圳)有限公司 | Digestive endoscope image recognition model training and recognition methods, apparatus and system |
-
2019
- 2019-12-23 CN CN201911338548.6A patent/CN111091562B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102670155A (en) * | 2012-03-19 | 2012-09-19 | 中国人民解放军南京军区南京总医院 | Method for establishing object and image correlation factor for endoscope measurement and applications thereof in endoscope measurement |
| CN103505217A (en) * | 2012-06-28 | 2014-01-15 | 吕海涛 | Method for accurately measuring size of nidus under endoscope |
| CN104083172A (en) * | 2013-11-28 | 2014-10-08 | 北京华科创智健康科技股份有限公司 | Method and device for quantitatively measuring size of focus through electronic endoscope |
| CN106456271A (en) * | 2014-03-28 | 2017-02-22 | 直观外科手术操作公司 | Alignment of q3d models with 3d images |
| CN104146711A (en) * | 2014-08-01 | 2014-11-19 | 深圳市开立科技有限公司 | Endoscope based lesion size measurement method and system |
| WO2018008195A1 (en) * | 2016-07-05 | 2018-01-11 | オリンパス株式会社 | Image processing device, image processing system, method for operating image processing device, and program for operating image processing device |
| CN109381152A (en) * | 2017-08-04 | 2019-02-26 | 卡普索影像公司 | For the area of the object of interest in stomach image or the method for volume and equipment |
| CN109141250A (en) * | 2018-08-08 | 2019-01-04 | 深圳码隆科技有限公司 | Luggage dimension measurement method, device and luggage dimensional measurement server |
| CN110473192A (en) * | 2019-04-10 | 2019-11-19 | 腾讯医疗健康(深圳)有限公司 | Digestive endoscope image recognition model training and recognition methods, apparatus and system |
| CN110200580A (en) * | 2019-06-14 | 2019-09-06 | 石焕 | A kind of householder method for tumor in digestive tract clinic early detection |
Non-Patent Citations (2)
| Title |
|---|
| WENMING YANG等: "Lesion Classification of Wireless Capsule Endoscopy Images", 《2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING（ISBI 2019）》 * |
| 王向前等: "内镜专用直接测量尺的设计及临床应用价值", 《中华消化病与影像杂志》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260648A (en) * | 2020-05-06 | 2020-06-09 | 杭州迪英加科技有限公司 | Method and device for measuring lesion area of gastric mucosa ESD excision specimen image |
| CN111260648B (en) * | 2020-05-06 | 2020-08-21 | 杭州迪英加科技有限公司 | Method and device for measuring lesion area of gastric mucosa ESD excision specimen image |
| CN112257506A (en) * | 2020-09-21 | 2021-01-22 | 北京豆牛网络科技有限公司 | Fruit and vegetable size identification method and device, electronic equipment and computer readable medium |
| CN112153320A (en) * | 2020-09-23 | 2020-12-29 | 北京京东振世信息技术有限公司 | Method and device for measuring size of article, electronic equipment and storage medium |
| CN112153320B (en) * | 2020-09-23 | 2022-11-08 | 北京京东振世信息技术有限公司 | Method and device for measuring size of article, electronic equipment and storage medium |
| CN112580437A (en) * | 2020-11-26 | 2021-03-30 | 复旦大学附属中山医院 | Method and device for measuring size of target |
| CN112863699A (en) * | 2020-12-31 | 2021-05-28 | 山东大学齐鲁医院 | ESD preoperative discussion system based on mobile terminal |
| CN114052641A (en) * | 2021-11-04 | 2022-02-18 | 深圳市博盛医疗科技有限公司 | Laparoscope system for reducing three-dimensional distortion in 3D vision based on intelligent optimization technology |
| CN114740614A (en) * | 2022-05-07 | 2022-07-12 | 深圳市微视光电科技有限公司 | Endoscope probe capable of assisting in measurement |
| CN114740614B (en) * | 2022-05-07 | 2024-04-19 | 深圳市微视光电科技有限公司 | Endoscope probe capable of assisting in measurement |
| CN116958147A (en) * | 2023-09-21 | 2023-10-27 | 青岛美迪康数字工程有限公司 | Target area determining method, device and equipment based on depth image characteristics |
| CN116958147B (en) * | 2023-09-21 | 2023-12-22 | 青岛美迪康数字工程有限公司 | Target area determining method, device and equipment based on depth image characteristics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111091562B (en) | 2020-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111091562B (en) | Method and system for measuring size of digestive tract lesion | |
| US9672620B2 (en) | Reconstruction with object detection for images captured from a capsule camera | |
| EP2466541B1 (en) | Image processing apparatus, image processing method and image processing program | |
| JP5281826B2 (en) | Image processing apparatus, image processing program, and image processing method | |
| JP5851160B2 (en) | Image processing apparatus, operation method of image processing apparatus, and image processing program | |
| CN109124662B (en) | Rib center line detection device and method | |
| CN103945755B (en) | Image processing apparatus | |
| CN111291736B (en) | Image correction method and device and medical equipment | |
| US20180182091A1 (en) | Method and system for imaging and analysis of anatomical features | |
| JP2007244519A (en) | Image analysis apparatus | |
| CN108601509B (en) | Image processing apparatus, image processing method, and program-recorded medium | |
| CN113017702B (en) | Method and system for identifying extension length of small probe of ultrasonic endoscope and storage medium | |
| CN110443802B (en) | Image detection method and device, equipment and storage medium | |
| Ali et al. | A pilot study on automatic three-dimensional quantification of Barrett’s esophagus for risk stratification and therapy monitoring | |
| JP2020516408A (en) | Endoscopic measurement method and instrument | |
| CN114022547A (en) | Endoscope image detection method, device, equipment and storage medium | |
| CN111311626A (en) | Skull fracture automatic detection method based on CT image and electronic medium | |
| CN115170629A (en) | Wound information acquisition method, device, equipment and storage medium | |
| JP6888041B2 (en) | How to get a medical sagittal image, how to train a neural network to get a medical sagittal image, and a computer device | |
| JP2007105352A (en) | Difference image display device, difference image display method, and program thereof | |
| CN116958147B (en) | Target area determining method, device and equipment based on depth image characteristics | |
| US9386908B2 (en) | Navigation using a pre-acquired image | |
| CN111466952B (en) | Real-time conversion method and system for ultrasonic endoscope and CT three-dimensional image | |
| CN112215878A (en) | X-ray image registration method based on SURF feature points | |
| CN107529962B (en) | Image processing apparatus, image processing method, and recording medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Zuo Xiuli Inventor after: Feng Jian Inventor after: Ma Mingjun Inventor after: Li Yanqing Inventor after: Li Zhen Inventor after: Shao Xuejun Inventor after: Yang Xiaoyun Inventor after: Lai Yonghang Inventor after: Xin Wei Inventor before: Zuo Xiuli Inventor before: Feng Jian Inventor before: Ma Mingjun Inventor before: Li Yanqing Inventor before: Li Zhen Inventor before: Shao Xuejun Inventor before: Yang Xiaoyun Inventor before: Lai Yonghang Inventor before: Xin Wei |