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Ultrasound image processing method of thyroid nodules based on cross-layer sparse atrous convolution

A technology for thyroid nodules and ultrasound images, applied in the field of ultrasound medical image information processing, can solve problems such as poor semantic extraction of ultrasound images

Active Publication Date: 2020-10-23
HANGZHOU CHUANGYING HEALTH MANAGEMENT CO LTD
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  • Description
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Problems solved by technology

[0003] The problem to be solved by the present invention is to provide a method for processing ultrasound images of thyroid nodules based on cross-layer sparse atrous convolution to solve the problem of poor semantic extraction effect of ultrasound images of thyroid nodules in existing deep learning networks

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  • Ultrasound image processing method of thyroid nodules based on cross-layer sparse atrous convolution
  • Ultrasound image processing method of thyroid nodules based on cross-layer sparse atrous convolution
  • Ultrasound image processing method of thyroid nodules based on cross-layer sparse atrous convolution

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Embodiment 1

[0053] Reference attachment figure 1 The ultrasonic image processing method of thyroid nodules based on cross-layer sparse cavity convolution shown in this embodiment includes the following steps:

[0054] The first step is to collect the original ultrasound images containing the thyroid nodules, establish an image training set, a validation set, and a test set based on the collected ultrasound original images, and outline the thyroid nodules in each image set.

[0055] Specifically, at least 15,000 images containing ultrasound images of thyroid nodules are collected, and the shapes of thyroid nodules in all images are delineated. Among them, at least 10,000 images are randomly selected as the training set, and the remaining images are randomly selected as the verification set. , The remaining images randomly select at least 3000 images as the test set.

[0056] The second step is to establish an image preprocessing module to preprocess the input ultrasound original image.

[0057] S...

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Abstract

The invention discloses a thyroid nodule ultrasonic image processing method based on cross-layer sparse atrous convolution, by establishing a novel cross-layer atrous convolution network structure, sparse constraint network, separating sparse atrous convolution layers, and self-adaptive weight adjustment And the loss function of sparse constraints overcomes the poor semantic resolution ability of the existing methods for the nodule area of ​​the thyroid nodule ultrasound image, and the semantic feature extraction of the nodule area is susceptible to similar background interference, and solves the problem of deep learning network in the forward propagation step. Due to the limited receptive field expansion ability, the semantic probability heat map extraction effect of thyroid nodule ultrasound images is not good and other problems.

Description

Technical field [0001] The invention relates to the field of ultrasonic medical image information processing, in particular to an ultrasonic image processing method of thyroid nodules based on cross-layer sparse cavity convolution. Background technique [0002] Thyroid nodules are a common clinical pathology, most of which are benign nodules and some are malignant thyroid cancer. Ultrasound examination is the first choice for the diagnosis of thyroid nodules. How to use computer and image processing methods to accurately extract the semantic probability heat map of the nodule area in the ultrasound image is used to locate, identify, segment, benign and malignant thyroid nodules and automatically distinguish nodules The basis for applications such as classification. At present, the method based on deep convolutional neural network is an effective method for extracting semantic probability heat map. The method first defines the deep learning network structure and corresponding los...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/62G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/62G06N3/08G06T2207/10132G06T2207/20076G06T2207/20081G06T2207/20084G06T2207/30096G06N3/047G06N3/048G06N3/045G06F18/2415G06F18/241
Inventor 姚劲草徐栋欧笛李伟杨琛汪丽菁王立平周玲燕朱乔丹张含芝张小
Owner HANGZHOU CHUANGYING HEALTH MANAGEMENT CO LTD
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