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SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.
Zhao, Zhihe; Du, Siyao; Xu, Zeyan; Yin, Zhi; Huang, Xiaomei; Huang, Xin; Wong, Chinting; Liang, Yanting; Shen, Jing; Wu, Jianlin; Qu, Jinrong; Zhang, Lina; Cui, Yanfen; Wang, Ying; Wee, Leonard; Dekker, Andre; Han, Chu; Liu, Zaiyi; Shi, Zhenwei; Liang, Changhong.
Afiliación
  • Zhao Z; School of Medicine, South China University of Technology, Guangzhou, 510006, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intell
  • Du S; Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China.
  • Xu Z; Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China.
  • Yin Z; Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China.
  • Huang X; Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Huang X; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Shantou
  • Wong C; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
  • Liang Y; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
  • Shen J; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
  • Wu J; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
  • Qu J; Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
  • Zhang L; Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China.
  • Cui Y; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated
  • Wang Y; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
  • Wee L; Clinical Data Science, Faculty of Health Medicine Life Sciences, Maastricht University, Maastricht, 6229 ET, The Netherlands; Department of Radiation Oncology (Maastro), GROW School of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands.
  • Dekker A; Department of Radiation Oncology (Maastro), GROW School of Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands.
  • Han C; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical
  • Liu Z; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China. Electron
  • Shi Z; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical
  • Liang C; School of Medicine, South China University of Technology, Guangzhou, 510006, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intell
Comput Biol Med ; 169: 107939, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38194781
ABSTRACT
Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https//github.com/GDPHMediaLab/SwinHR).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Neoplasias Mamarias Animales Tipo de estudio: Diagnostic_studies Límite: Animals / Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Neoplasias Mamarias Animales Tipo de estudio: Diagnostic_studies Límite: Animals / Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos