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Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI.
Li, Yuchun; Wu, Yuanyuan; Huang, Mengxing; Zhang, Yu; Bai, Zhiming.
Afiliación
  • Li Y; State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information and Communication Engineering, Hainan University, Haikou 570288, China.
  • Wu Y; State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information and Communication Engineering, Hainan University, Haikou 570288, China.
  • Huang M; State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information and Communication Engineering, Hainan University, Haikou 570288, China. Electronic address: huangmx09@hainanu.edu.cn.
  • Zhang Y; School of Computer science and Technology, Hainan University, Haikou 570288, China.
  • Bai Z; Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou 570288, China.
Comput Biol Med ; 165: 107374, 2023 10.
Article en En | MEDLINE | ID: mdl-37611428
BACKGROUND AND OBJECTIVE: Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging. METHODS: This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism. RESULTS: The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively. CONCLUSIONS: Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Aprendizaje Profundo Tipo de estudio: Guideline Límite: Humans / Male Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
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: Neoplasias de la Próstata / Aprendizaje Profundo Tipo de estudio: Guideline Límite: Humans / Male Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos