New coronal pneumonia focus segmentation method based on feature fusion deep supervision U-Net

Abstract

The invention relates to the technical field of medical image segmentation, and provides a new coronal pneumonia focus segmentation method based on feature fusion deep supervision U-Net, and the method comprises: 1, obtaining an initial sample set; 2, preprocessing the initial sample set; 3, obtaining a training sample set and a verification sample set; 4, performing data augmentation on the training sample set; 5, building a COVID-19 lesion area automatic segmentation model in the chest CT image based on feature fusion deep supervision U-Net, wherein the automatic segmentation model comprises an encoder, a decoder, jump connections, feature fusion blocks and deep supervision branches, adding the feature fusion blocks between the jump connections of different levels, and adding the deep supervision branches to the decoder part; 6, training a segmentation model; and 7, segmenting the chest three-dimensional CT scanning image to be segmented. According to the method, automatic segmentation of the COVID-19 lesion area in the chest CT can be realized, and the accuracy and rapidity of segmentation are improved.

Description

technical field [0001] The present invention relates to the technical field of medical image segmentation, in particular to a novel coronavirus pneumonia lesion segmentation method based on feature fusion deep supervision U-Net. Background technique [0002] At the end of 2019, the novel coronavirus (COVID-19) outbreak began worldwide. As of February 26, 2021, the number of confirmed cases worldwide has exceeded 100 million. On the one hand, each suspected case needs to be confirmed by RT-PCR testing. Although RT-PCR is the gold standard for diagnosis, the process is very time-consuming and the false-negative diagnosis is high. On the other hand, chest CT scans of COVID-19 patients usually show patchy ground-glass opacities on both sides of the lungs, which has been used as an important supplementary indicator for COVID-19 screening due to the high sensitivity of this feature. [0003] Chest CT examination also plays an important role in the follow-up evaluation of hospital...

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However, the structure only uses single-level single-scale features, and the importance of different levels of features is different for different data.

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