Burn area segmentation system based on pulse neural network U-shaped model

A technology of spiking neural network and region segmentation, applied in biological neural network models, neural architectures, neural learning methods, etc., can solve the problems of complex detection methods and high costs, and achieve the effect of improving accuracy and precision.

Pending Publication Date: 2022-03-15
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the existing detection methods, which are similar to tissue biopsy technology, living body staining detection technology, ...

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  • Burn area segmentation system based on pulse neural network U-shaped model
  • Burn area segmentation system based on pulse neural network U-shaped model
  • Burn area segmentation system based on pulse neural network U-shaped model

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[0039] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0040] A burn area segmentation system based on the U-shaped model of the spiking neural network. After using the camera to collect the burn pictures of the patient, after preprocessing and data enhancement of the burn pictures, they are input into the U-shaped model of the spiking neural network to obtain The grayscale image of the burn area is segmented, and then binarized by calculating the adaptive threshold through Pcnn to obtain a binarized segmented image, and finally output the segmented image.

[0041] Such as figure 1 Shown, the system running process of the present invention comprises the following steps:

[0042] S1: Collect burn pictures, use the ...

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Abstract

The invention discloses a burn area segmentation system based on a pulse neural network U-shaped model, which comprises an image acquisition device and a neural network, and is characterized in that the image acquisition device is used for acquiring a burn image of a burn part of a patient, and the neural network comprises the pulse neural network U-shaped model and a pulse coupling neural network; the pulse neural network U-shaped model comprises a neuron membrane potential updating and pulse emitting layer, a lower sampling layer, an upper sampling layer, a feature fusion layer and a loss optimization module, training of burn area segmentation is carried out through the collected image, a burn area is segmented through the trained pulse neural network U-shaped model, and a burn area segmentation result is obtained. And the pulse coupling neural network calculates an adaptive threshold to binarize the segmentation result of the burn region to obtain a visualization result.

Description

technical field [0001] The invention relates to the field of burn medical image processing, in particular to a burn region segmentation system based on a U-shaped model of a pulse neural network. Background technique [0002] Burns are damage to human tissue caused by heat (flame, hot liquid, hot steam), electric current, radiation, chemical substances, etc. Burns are not only damage to the skin, but can also reach deep into the muscles and bones, and severe cases can cause shock and infection. It takes 21 days for a deep burn wound to heal, and surgical treatment is often required. After recovery, the skin loses its elasticity, is dry and has no exudate, requires surgical skin grafting treatment, and scars appear after healing. [0003] Medical image processing of burns has a crucial impact on the treatment of burns. In general, correct early treatment can reduce the degree of burn injury, complications and mortality. However, the existing detection methods, which are si...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/00G06T5/40G06T3/00G06N3/04G06N3/08G06N3/06G06V10/26G06V10/28G06V10/12G06V10/82
CPCG06T7/11G06T7/0012G06T5/40G06T3/0012G06N3/049G06N3/084G06N3/061G06T2207/20081G06T2207/20084G06T2207/20132G06T2207/30101G06N3/045
Inventor 梁嘉铠岳克强李瑞雪李文钧李懿霖赵金铎甘智高许雨婷
Owner HANGZHOU DIANZI UNIV
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