SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation

Authors:
raw_author_name: Zhihe Zhao
raw_affiliation_string: 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; School of Medicine, South China University of Technology, Guangzhou, 510006, China
raw_author_name: Zeyan Xu
raw_affiliation_string: Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
raw_author_name: Zhi Yin
raw_affiliation_string: 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
raw_author_name: Xiaomei Huang
raw_affiliation_string: Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
raw_author_name: Xin Huang
raw_affiliation_string: 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 University Medical College, Shantou, 515041, China
raw_author_name: Jing Shen
raw_affiliation_string: Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
raw_author_name: Jianlin Wu
raw_affiliation_string: Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China
raw_author_name: Jinrong Qu
raw_affiliation_string: Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
raw_author_name: Lina Zhang
raw_affiliation_string: Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China
raw_author_name: Yanfen Cui
raw_affiliation_string: 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; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
raw_author_name: Ying Wang
raw_affiliation_string: Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China
raw_author_name: Leonard Wee
raw_affiliation_string: 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
raw_author_name: Andre Dekker
raw_affiliation_string: Clinical Data Science, Faculty of Health Medicine Life Sciences, Maastricht University, Maastricht, 6229 ET, The Netherlands
raw_author_name: Chu Han
raw_affiliation_string: Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
raw_author_name: Zaiyi Liu
raw_affiliation_string: 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
raw_author_name: Zhenwei Shi
raw_affiliation_string: Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
raw_author_name: Changhong Liang
raw_affiliation_string: 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; School of Medicine, South China University of Technology, Guangzhou, 510006, China

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).