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[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].
Tao, Huimin; Huang, Miao; Liu, Cong; Liu, Yongtian; Hu, Zhihua; Tao, Lili; Zhang, Shuping.
Afiliación
  • Tao H; School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209.
  • Huang M; School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209.
  • Liu C; School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209.
  • Liu Y; Department of Internet of Things Engineering, Shanghai Business School, Shanghai, 201400.
  • Hu Z; Urinary Surgery, Shandong First Medical University Affiliated Qingzhou Hospital, Qingzhou, 262500.
  • Tao L; School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209.
  • Zhang S; School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209.
Zhongguo Yi Liao Qi Xie Za Zhi ; 48(2): 138-143, 2024 Mar 30.
Article en Zh | MEDLINE | ID: mdl-38605611
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Idioma: Zh Revista: Zhongguo Yi Liao Qi Xie Za Zhi Año: 2024 Tipo del documento: Article Pais de publicación: China
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Idioma: Zh Revista: Zhongguo Yi Liao Qi Xie Za Zhi Año: 2024 Tipo del documento: Article Pais de publicación: China