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Hand bone key region acquisition method based on convolutional neural network and multi-granularity attention

A convolutional neural network and attention technology, applied in the field of hand bone key area acquisition based on convolutional neural network and multi-granularity attention, can solve problems such as expensive, insufficiently fine and diverse areas, and achieve the effect of improving accuracy

Pending Publication Date: 2022-05-27
HEFEI UNIV OF TECH +1
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AI Technical Summary

Problems solved by technology

However, manual annotation not only requires expert knowledge, but is also expensive and subjective
Moreover, the regions located by the existing attention-based methods are not fine and diverse enough.

Method used

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  • Hand bone key region acquisition method based on convolutional neural network and multi-granularity attention
  • Hand bone key region acquisition method based on convolutional neural network and multi-granularity attention
  • Hand bone key region acquisition method based on convolutional neural network and multi-granularity attention

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Experimental program
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Embodiment Construction

[0045] In this embodiment, a method for acquiring key regions of hand bone based on convolutional neural network and multi-granularity attention can locate the most discriminative ossification region only by using the bone age annotation information of the hand bone picture, and locate the most differentiated ossification region. The area of ​​​​is consistent with the key area used by the TW3 method. Specifically, as figure 1 shown, proceed as follows:

[0046] Step 1: Normalize the size of all hand bone X-ray images, and apply random affine transformation and random horizontal flip for data enhancement to obtain an input image set, and any image in the input image set is marked as I;

[0047] In this example, the dataset used is the public bone age dataset provided by the Radiological Society of North America in 2017. All hand images contain bone age and gender information. The original spatial resolution of the hand bone images in this dataset is not the same. Since the c...

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Abstract

The invention discloses a hand bone key region acquisition method based on a convolutional neural network and multi-granularity attention. The method comprises the following steps: 1, acquiring a data set containing bone age information of a hand bone; 2, constructing a bone age evaluation network containing multi-granularity attention; 3, performing off-line training on the established bone age evaluation network; and 4, acquiring a key area of the hand bone by utilizing attention in the trained network. According to the method, the key region of the hand bone can be obtained only through the bone age label of the hand bone, the obtained key region is consistent with a key region used by a TW3 method, and the difficulty that an existing key region obtaining method depends on manual labels is overcome.

Description

technical field [0001] The invention relates to the field of medical image analysis, in particular to a method for acquiring key areas of hand bones based on a convolutional neural network and multi-granularity attention. Background technique [0002] Bone age assessment on hand radiographs is often used to investigate growth in children. In clinical practice, the diagnosis and treatment of growth disorders can be aided and monitored by exploring the delayed or accelerated appearance of non-handed ossification centers. [0003] The TW method assesses the maturity level of specific bones of the hand and wrist. The maturity levels of different bones correspond to different maturity scores. The skeletal maturity is converted into scores and the scores of all key areas are added to calculate the total maturity score. The bone age is finally calculated from the total maturity score. The TW3 method is relatively more complicated and requires more time. Since the maturity of eac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04G06K9/62G06V10/80
CPCG06T7/0012G06T2207/30008G06N3/044G06N3/045G06F18/253
Inventor 王晓华范伟程峰胡敏盛海王宇航
Owner HEFEI UNIV OF TECH
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