Enteroscope withdrawal quality intelligent monitoring system and method based on deep neural network
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A technology of deep neural network and intelligent monitoring system, which is applied in the direction of biological neural network model, neural architecture, image data processing, etc., and can solve the problems of colorectal endoscopy chaos and disorder
Active Publication Date: 2021-04-02
SICHUAN UNIV
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However, colorectal endoscopy is highly dependent on the professional ability of doctors, and there is no objective and fair evaluation index for the doctor's operation process in the process of colorectal endoscopy, resulting in confusion and disorder in colorectal endoscopy
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Embodiment 1
[0040] This embodiment proposes a deep neural network-based intelligent monitoring system for the quality of colonoscope retraction. The schematic diagram of the system architecture is shown in figure 1 , where the system includes:
[0041]Background video processing and calling end, used to split the obtained video stream into image frames, and call the quality monitoring model;
[0042] The quality monitoring model end is used to run the target area detection model and classification model based on the deep neural network; the details include the following:
[0043] Invoke the target area detection model based on the deep neural network at startup, detect the target area in the frame image, and segment the detected target area from the corresponding image frame, and use the segmented target area for effective field of view Determine the validity, record the images of the effective field of view and the total number of images, and return the ratio of the number of images of ...
Embodiment 2
[0063] On the basis of Example 1, this embodiment proposes a method for intelligently monitoring the quality of colonoscope withdrawal based on deep neural networks, the flow chart of which is shown in Figure 5 , wherein the method includes the following steps:
[0064] S1. Acquire the video stream generated during colonoscopy.
[0065] S2. Split the acquired video stream into image frames.
[0066] S3. Detect the target area in each frame of image, and segment the detected target area from the corresponding image frame. Wherein, the frame image may include a target area part, a state information part and a control information part.
[0067] S4. Determine the validity of the field of view for the segmented target area, and record the images of the effective field of view and the total number of images.
[0068] S5. The ratio of the number of images in the effective field of view to the total number of images is returned to the video stream display end as the effective rate...
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Abstract
The invention relates to the field of colonoscopy process quality evaluation, and provides an enteroscope withdrawal quality intelligent monitoring system and method based on a deep neural network, and the system comprises a quality monitoring model end, a background video processing and calling end, and a video stream display end. The method comprises the following steps: acquiring a video streamgenerated during colonoscopy and splitting the video stream into image frames; detecting a target area in each frame of image and segmenting the target area; judging the effectiveness of the visual field of the segmented target area, and returning the ratio of the number of the images of the effective visual field to the total number of the images to the video stream display end as the visual field efficiency in the lens retreating process; equally dividing the image judged as the effective visual field into four parts according to four quadrants, respectively carrying out lumen, local and fuzzy three-classification judgment, and returning the ratio of the number of images of local classification to the number of images of the effective visual field and the ratio of the number of images of lumen classification to the number of images of the effective visual field to the video stream display end.
Description
technical field [0001] The invention relates to the field of quality evaluation of the colonoscopy process, in particular to a deep neural network-based intelligent monitoring system and method for the quality of colonoscopy retraction. Background technique [0002] Colorectal cancer (CRC) is a relatively common cancer and the third most common type of cancer death. According to statistics, its incidence rate ranks third and second among cancers in men and women, respectively. In 2015, there were 1.65 million new cases of colorectal cancer and nearly 835,000 deaths. The main incidence of early colon cancer is the European and American population, but the incidence of colorectal cancer in Asian populations has been increasing in recent years. Colon polyps are the precursors of colorectal cancer, so for early colorectal patients, it is very important for clinicians to find polyps through colonoscopy. Early detection and early treatment can only be achieved by performing colo...
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