Abstract: The present invention discloses a capsule endoscope, which comprises an enclosure, and an imaging unit, a data processing unit and an antenna unit arranged in the enclosure. The enclosure comprises a cylindrical middle enclosure and two hemispherical covers connected to both ends of the middle enclosure, and the antenna unit is arranged close to the inner surface of the middle enclosure. The antenna unit is arranged close to the inner surface of the middle enclosure. Such arrangement can save internal space and improve the space utilization, and also, the arrangement of the antenna unit does not affect the layout of the imaging unit.

Abstract: A method of three-dimensional reconstruction of capsule endoscope images is described. The method combines two algorithms for obtaining image depth information values to improve the calculation accuracy of image depth values, and thus to increase the image three-dimensional reconstruction rate and improve the image identification precision.

Abstract: The present invention provides an endoscopic image recognition method, an electronic device, and a storage medium. The method includes: performing disease prediction for a plurality of disease categories for a plurality of original images respectively using a first neural network model; establishing test sample sets for the disease categories based on the disease prediction results for the original images, where each test sample set contains image features of a predefined number of original images; performing disease recognition for the test sample sets of the disease categories respectively using a second neural network model; superimposing the disease recognition results for the disease categories to obtain a case diagnosis result; where the second neural network model performs a weighted combination of a plurality of image features within the test sample sets to obtain the disease recognition results. The method improves the accuracy of disease recognition.

Abstract: The present invention provides a deep learning-based cancer prognosis survival prediction method and a device, and a storage medium. The method includes: data acquisition: acquiring sample data, where the sample data comprising pathological image data and clinical data of a sample; data preprocessing; prediction model training: training and evaluating a prediction model to obtain an optimal prediction model; and risk prediction: performing risk prediction on a new sample on the basis of an optimal classifier model and the optimal prediction model. In the method, data features of the pathological image data and clinical data are unified, the prediction model is trained and evaluated to obtain the optimal prediction model, and prognosis risk evaluation is performed on the new sample to enhance the efficiency of diagnosis and treatment in the clinical field and improve the accuracy of risk evaluation results.

Abstract: An image processing method, an electronic device, and a readable storage medium are provided. The method includes: obtaining an original image; denoising the original image using an image filtering algorithm to form a preprocessed image S; performing contrast enhancement in the preprocessed image S to form a contrast-enhanced image J; adjusting saturation of the contrast-enhanced image J to form an enhanced display image M; sharpening the enhanced display image M to form a sharpened image N; and performing dynamic range enhancement on the sharpened image N using a dynamic range image enhancement algorithm to form an output image. The image processing method, electronic device, and readable storage medium enable multi-level processing on the original image and improve the display accuracy of the output image.

Abstract: A control method and a control system for a capsule endoscope are provided. The control method comprises acquiring a frame rate of the capsule endoscope. The frame rate is the number of frames of images captured by the capsule endoscope per unit time. The control method further comprises controlling the number of radio frequency transmission channels that are turned on according to the frame rate. The number of radio frequency transmission channels that are turned on is at least two, and the radio frequency transmission channels transmit the captured images. The control method and control system for the capsule endoscope can adaptively distribute a plurality of radio frequency transmission channels according to the real-time position of the capsule endoscope, and further turn on the radio frequency transmission channels to transmit a plurality of signals synchronously or asynchronously.

Abstract: The present invention discloses a method, device and medium for structuring a capsule endoscopy report text. The method includes: annotating the report text using an annotation model; storing each named entity classification label in the report text in a hierarchical tree structure according to the annotation information to form a tree structure diagram; parsing the tree structure diagram, extracting abnormal structure data and time parameters, and storing the abnormal structure data and time parameters in a panel data structured manner to form an abnormal structure panel table and a time parameter panel table. The present invention can automatically annotate the capsule endoscopy report through the annotation model, and output parameters of different amount of information in different structures, and quantitative and accurate quality control of the capsule endoscopy process and examination results, which provide sufficient convenience for the electronic medical information of the capsule endoscopy.

Abstract: Provided is a method for building an image reading model based on a capsule endoscope, a device and a medium. The method includes: driving the capsule endoscope to move within a working area, sequentially recording the position coordinates and field of view orientations of the capsule endoscope when it reaches each positioning point at a predetermined first frequency, and driving the capsule endoscope to sequentially capture images at a predetermined second frequency and record the images; constructing a 3D model corresponding to the outer contour of the working area based on the recorded position coordinates of the capsule endoscope at each positioning point; and mapping the recorded images onto the 3D model to create an image reading model. By mapping the obtained images onto a 3D model of the working area, to enhance the visualization effect of the examination, facilitate observation, save image reading time, and increase the detection efficiency.

Abstract: The present invention provides a completeness self-checking method of a capsule endoscope, an electronic device, and a readable storage medium. The method comprises: driving the capsule endoscope to move within a working area and capturing images upon reaching each working point, and synchronously executing a step A; the step A comprises: recording the position and field of view orientation of each working point; determining an intersection area between the field of view of the capsule endoscope and a virtual positioning area; obtaining each voxel that is not labeled with illuminated identifier in each intersection area, and obtaining a line of sight vector from the current working point to each voxel that is not labeled with illuminated identifier, and merging the line of sight vectors into the same vector set; labeling the voxels corresponding to the current vector set with illuminated identifiers. It implements the completeness self-checking of the capsule endoscope.

Abstract: A capsule endoscope image recognition method based on deep learning, and a device and a medium is provided. After a plurality of frames of continuously captured images are processed to form an image sequence of a specific format, multi-channel recognition is performed on the plurality of frames of images by means of a 3D convolutional neural network model, and a recognition result is then output in combination with a recognition probability of each channel, such that the image recognition precision is improved.

Abstract: The present invention provides a three-dimensional reconstruction method of a capsule endoscope image, an electronic device, and a readable storage medium, which combines two algorithms for obtaining image depth information values to improve the calculation accuracy of image depth information values, and thus to increase the image three-dimensional reconstruction rate and improve the image identification precision.

Abstract: A referenceless image evaluation method for a capsule endoscope, an electronic device, and a medium. The method comprises: acquiring image quality evaluation scores and image content evaluation scores corresponding to respective original images; and determining, according to weights of the image content evaluation scores and weights of the image quality evaluation scores, a comprehensive score for each image currently being evaluated. The referenceless image evaluation method for a capsule endoscope employs various evaluation models to evaluate the image quality and content of a number of original images taken in the same detection region. The results of these evaluations are then combined to produce comprehensive scores for the numerous original images of the same region. Through the comprehensive scores, high-quality images can be quickly screened out, and thus the screening of the original images can be accelerated and the identification accuracy be improved.

Abstract: The present invention provides an image recognition method, an electronic device and a computer-readable storage medium. The method includes: segmenting an original image into a plurality of unit images having the same predetermined size; inputting the unit images into a pre-built neural network model to carry out processing, so as to correspondingly add a detection frame to a marker in each unit image to form a pre-detection unit image; stitching a plurality of pre-detection unit images into a pre-output image according to segmentation positions of each unit image in the original image; determining whether the markers selected in two adjacent detection frames in the pre-output image are the same marker; outputting the image with the detection frames until all the detection frames confirmed to have the same markers are all merged. The present invention can effectively recognize types and positions of the markers in the image.

Abstract: The present invention provides an image processing method, an electronic device, and a readable storage medium. The method comprises: obtaining an original image, determining whether the current original image contains a cavity; if the current original image contains a cavity, increasing the frame rate based on the original frame rate; if the current original image does not contain a cavity, maintaining the original frame rate for image capturing. The present invention can automatically adjust the frame rate according to the actual images captured by a capsule endoscope, improving the capture of intestinal images, reducing the probability of missing shots, and achieving the goal of saving power, while enhancing the efficiency of capsule endoscope usage.

Abstract: A method for stitching images of a capsule endoscope, an electronic device, and a readable storage medium are provided. The method comprises: performing image rectification and circular edge masking on original images to form pre-processed images, and performing image enhancement (S1); completing detection and pairing of feature points (S2); calculating a transformation model of all pre-processed images to a same optimal plane according to a set of the feature points (S3); performing projective transformation for each enhanced image to a same coordinate system (S4); and stitching images according to an obtained sequence to form a fused image for output (S5), thus expanding the field of view of a single image of the capsule endoscope.

Abstract: The present invention discloses a capsule endoscope system, a method of identifying stained area in endoscopic images, and a computer readable storage medium. The method comprises: obtaining a basic image taken by a photographing device; removing abnormal pixels from the basic image and recording it as a processed image; obtaining the hue, saturation and value of each pixel in the processed image of HSV format; setting a first condition that the hue is in a D1 range, the saturation is in a D2 range, and the value is in a D3 range; obtaining the b component of each pixel in the processed image of Lab format; setting a second condition that the b component is in a D4 range; collecting and processing the pixels that meet the first condition and/or the second condition to form a set C; calculating the initial range area S1 of the set C.

Abstract: A method and system for determining quality of a capsule endoscope image. The method comprises: acquiring a first group of images of a standard color card that are photographed by a capsule endoscope in a non-magnetic-field environment and a second group of images of the standard color card that are photographed by the capsule endoscope in a magnetic field environment; calculating a peak signal-to-noise ratio of each color region of the standard color card in the non-magnetic-field environment and a peak signal-to-noise ratio of each color region of the standard color card in the magnetic field environment according to the acquired first group of images and the acquired second group of images; and determining, according to the calculated peak signal-to-noise ratios, whether a magnetic field affects the quality of a capsule endoscope image. The effect of the magnetic field on the quality of an image can be effectively determined.

Abstract: The present invention provides an image-based position detection method, an electronic device, and a readable storage medium. The method includes: driving a capsule endoscope to continuously capture a group of images at an interval of a first preset time t1, where each group of images comprises at least one image; obtaining at least one image from each group of images, and calculating a determination index according to grayscale values of at least two channels of the image; determining whether the capsule endoscope reaches a preset detection position according to a value of the determination index; if yes, adjusting the capsule endoscope to a corresponding working mode according to a current position of the capsule endoscope, and if no, continue monitoring. The present invention can determine whether the capsule endoscope reaches the preset detection position, which greatly save battery power, and improve work efficiency of the capsule endoscope.

Abstract: The present invention discloses a package for a capsule endoscope. The package includes an inner package. The inner package includes a base and a cover arranged above the base. A first end of the cover is connected to a first side of the base and a second end of the cover is connected to a second side of the base in an assembled mode. When the second end of the cover is assembled and connected to the second side of the base, the bottom surface of the cover and the base form an holding slot for holding the capsule endoscope. Through such arrangement, the present invention improves the existing package and avoids a reduction of performance of the capsule endoscope during transportation.

Abstract: An automatic identification method and an identification system for a gastrointestinal marker are provided. The automatic identification method comprises the following steps: determining a suspected gastrointestinal marker region in an image; removing an overlapping suspected gastrointestinal marker region; and determining whether the suspected gastrointestinal marker region is part of the gastrointestinal marker. In the method, by means of processing and analyzing an image, positions of a gastrointestinal marker in the image can be automatically detected.