[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].

Huimin Tao, Miao Huang, Cong Liu, Yongtian Liu, Zhihua Hu, Lili Tao, Shuping Zhang
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引用次数: 0

Abstract

Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.

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[基于深度学习的 X 射线成像肾上腺血管关键帧识别算法]。
原发性醛固酮增多症的分期诊断需要肾上腺静脉取样,肾上腺静脉的取样框被称为关键框。目前,关键帧的选择主要依靠医生的视觉判断,费时费力。本研究提出了一种基于深度学习的关键帧识别算法。首先,利用小波去噪和多尺度血管增强滤波保留肾上腺静脉的形态特征。此外,通过结合自注意机制,得到了一种名为 ResNet50-SA 的改进识别模型。与常用的迁移学习相比,新模型的准确率、精确度、召回率、F1和AUC均达到97.11%,优于其他模型,可帮助临床医生快速识别肾上腺静脉中的关键帧。
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来源期刊
中国医疗器械杂志
中国医疗器械杂志 Medicine-Medicine (all)
CiteScore
0.40
自引率
0.00%
发文量
8086
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