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A hybrid attentional guidance network for tumors segmentation of breast ultrasound images.
Lu, Yaosheng; Jiang, Xiaosong; Zhou, Mengqiang; Zhi, Dengjiang; Qiu, Ruiyu; Ou, Zhanhong; Bai, Jieyun.
Afiliación
  • Lu Y; Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, 510632, China.
  • Jiang X; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
  • Zhou M; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
  • Zhi D; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
  • Qiu R; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
  • Ou Z; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
  • Bai J; College of Information Science and Technology, Jinan University, Guangzhou, 510632, China.
Int J Comput Assist Radiol Surg ; 18(8): 1489-1500, 2023 Aug.
Article en En | MEDLINE | ID: mdl-36853584
PURPOSE: In recent years, breast cancer has become the greatest threat to women. There are many studies dedicated to the precise segmentation of breast tumors, which is indispensable in computer-aided diagnosis. Deep neural networks have achieved accurate segmentation of images. However, convolutional layers are biased to extract local features and tend to lose global and location information as the network deepens, which leads to a decrease in breast tumors segmentation accuracy. For this reason, we propose a hybrid attention-guided network (HAG-Net). We believe that this method will improve the detection rate and segmentation of tumors in breast ultrasound images. METHODS: The method is equipped with multi-scale guidance block (MSG) for guiding the extraction of low-resolution location information. Short multi-head self-attention (S-MHSA) and convolutional block attention module are used to capture global features and long-range dependencies. Finally, the segmentation results are obtained by fusing multi-scale contextual information. RESULTS: We compare with 7 state-of-the-art methods on two publicly available datasets through five random fivefold cross-validations. The highest dice coefficient, Jaccard Index and detect rate ([Formula: see text]%, [Formula: see text]%, [Formula: see text]% and [Formula: see text]%, [Formula: see text]%, [Formula: see text]%, separately) obtained on two publicly available datasets(BUSI and OASUBD), prove the superiority of our method. CONCLUSION: HAG-Net can better utilize multi-resolution features to localize the breast tumors. Demonstrating excellent generalizability and applicability for breast tumors segmentation compare to other state-of-the-art methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Neoplasias de la Mama Tipo de estudio: Diagnostic_studies / Guideline Límite: Female / Humans Idioma: En Revista: Int J Comput Assist Radiol Surg Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Neoplasias de la Mama Tipo de estudio: Diagnostic_studies / Guideline Límite: Female / Humans Idioma: En Revista: Int J Comput Assist Radiol Surg Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania