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Laparoscopic surgery stage identification method and system based on dual-granularity time convolution

A recognition method and double-grained technology, applied in neural learning methods, character and pattern recognition, image data processing, etc., can solve problems such as difficult to accurately distinguish transition frames between stages, and achieve good recognition effect, improved recognition effect, and good generalization effect of ability

Pending Publication Date: 2022-04-19
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Using the visual and timing information of surgical videos, it can solve the problem that in the field of deep learning, the category of surgical stages can be identified but it is difficult to accurately distinguish the transition frames of stages

Method used

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  • Laparoscopic surgery stage identification method and system based on dual-granularity time convolution
  • Laparoscopic surgery stage identification method and system based on dual-granularity time convolution
  • Laparoscopic surgery stage identification method and system based on dual-granularity time convolution

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Embodiment 1

[0085] Such as Figure 1 to Figure 4 As shown, a double-granularity temporal convolution-based laparoscopic surgery stage recognition method disclosed in this embodiment, the specific circumstances are as follows:

[0086] 1) First, the laparoscopic minimally invasive surgery process is recorded through a micro-camera installed at the tip of the surgical instrument, and each complete surgical process is saved as a video. Then use ffpmeg to slice each video, and save a picture every 5 frames, arranged in order of frame number. Then remove abnormal pictures, including pictures with problems such as full-image blur, large-scale phantoms, extreme lighting, and incomplete shooting, to make a data set for the laparoscopic surgery stage, and split it into training at a ratio of 40:8:32. set, validation set, and test set. Finally, OpenCV is used to perform image enhancement operations such as center flip, random cropping, and shuffle order on the laparoscopic surgery images.

[008...

Embodiment 2

[0119] see Figure 5 As shown, this embodiment discloses a laparoscopic surgery stage recognition system based on double-granularity time convolution, including the following functional modules:

[0120] The data acquisition module is used to collect laparoscopic surgery videos, down-sample each video, and retain several images at each stage of each video to make a laparoscopic surgery data set, according to "address / video serial number / frame serial number" The format named permutation forms a video sequence;

[0121] The data processing module is used to input the video sequence in the laparoscopic surgery data set into the first part of the double-granularity temporal convolution network, that is, the dual-granularity temporal convolution module, to model the long-distance temporal context information and generate the initial prediction result, And use the cross-entropy loss function to calculate the gap between the initial prediction result and the actual data; input the i...

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Abstract

The invention discloses a laparoscopic surgery stage identification method and system based on dual-granularity time convolution. The method comprises the following steps: 1) constructing a laparoscopic surgery data set; 2) performing preliminary feature extraction on the picture sequence by using a dual-granularity time convolution module of the dual-granularity time convolution network, and outputting an initial prediction result of each frame of image; 3) correcting an initial prediction result output by the dual-granularity time convolution module by using a single-granularity time convolution module of the dual-granularity time convolution network; and 4) mapping a prediction result to an interval of (0, 1) to obtain a final operation stage identification result. The laparoscopic surgery stage recognition is realized by using the dual-granularity time convolution network, higher precision and better generalization ability under different backgrounds are realized, different types of surgery stages can be accurately detected, and the visual and time sequence information of the surgery video is utilized to realize the recognition of the laparoscopic surgery stage. The problem that in the deep learning field, operation stage categories can be recognized, but stage transition frames are difficult to distinguish accurately can be solved.

Description

technical field [0001] The present invention relates to the technical field of laparoscopic minimally invasive surgery image processing and neural network, in particular to a laparoscopic surgery phase recognition method and system based on double granularity time convolution. Background technique [0002] Laparoscopic minimally invasive surgery is a common minimally invasive surgical procedure that not only provides substantial medical benefits to the patient, but also provides the opportunity for the physician to record video of the procedure due to the need for a camera during the procedure. Reviewing and analyzing the surgical process through surgical video can improve the technical quality of surgeons and improve the safety of patients. However, manual retrieval of surgical videos is a very tedious and time-consuming task. With the increasing maturity of computer-aided technology, automated surgical stage recognition methods can better help doctors monitor and optimize ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/08G06N3/04G06K9/62G06V10/44G06V10/764
CPCG06T7/0012G06N3/08G06T2207/20081G06T2207/10016G06N3/045G06F18/2415
Inventor 吴秋遐韦喆艺
Owner SOUTH CHINA UNIV OF TECH
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