Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images

doi:10.1089/cmb.2023.0446

	Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
	Niyogisubizo, Jovial 1,2,3; Zhao, Keliang 1,2,3; Meng, Jintao 1,2; Pan, Yi 4; Didi, Rosiy Adi 5; Wei, Yanjie 1,2
	2024-10-18
发表期刊	JOURNAL OF COMPUTATIONAL BIOLOGY
ISSN	1066-5277
页码	13
摘要	Time-lapse microscopy imaging is a crucial technique in biomedical studies for observing cellular behavior over time, providing essential data on cell numbers, sizes, shapes, and interactions. Manual analysis of hundreds or thousands of cells is impractical, necessitating the development of automated cell segmentation approaches. Traditional image processing methods have made significant progress in this area, but the advent of deep learning methods, particularly those using U-Net-based networks, has further enhanced performance in medical and microscopy image segmentation. However, challenges remain, particularly in accurately segmenting touching cells in images with low signal-to-noise ratios. Existing methods often struggle with effectively integrating features across different levels of abstraction. This can lead to model confusion, particularly when important contextual information is lost or the features are not adequately distinguished. The challenge lies in appropriately combining these features to preserve critical details while ensuring robust and accurate segmentation. To address these issues, we propose a novel framework called RA-SE-ASPP-Net, which incorporates Residual Blocks, Attention Mechanism, Squeeze-and-Excitation connection, and Atrous Spatial Pyramid Pooling to achieve precise and robust cell segmentation. We evaluate our proposed architecture using an induced pluripotent stem cell reprogramming dataset, a challenging dataset that has received limited attention in this field. Additionally, we compare our model with different ablation experiments to demonstrate its robustness. The proposed architecture outperforms the baseline models in all evaluated metrics, providing the most accurate semantic segmentation results. Finally, we applied the watershed method to the semantic segmentation results to obtain precise segmentations with specific information for each cell.
关键词	cell segmentation deep learning watershed segmentation bright-field microscopy iPS cell reprogramming attention mechanism
DOI	10.1089/cmb.2023.0446
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[62272449] ; Strategic Priority CAS Project[XDB38050100] ; Key Research and Development Project of Guangdong Province[2021B0101310002] ; Shenzhen Basic Research Fund[RCYX20200714114734194] ; Shenzhen Basic Research Fund[KQTD20200820113106007] ; Youth Innovation Promotion Association[Y2021101] ; Key Laboratory of Quantitative Synthetic Biology, Chinese Academy of Sciences[CKL075] ; ANSO Scholarship for Young Talents
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:001335064300001
出版者	MARY ANN LIEBERT, INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39496
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wei, Yanjie
作者单位	1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Intelligent Bioinformat, 1068 Xueyuan Rd, Shenzhen 518055, Peoples R China 2.Shenzhen Inst Adv Technol, Chinese Acad Sci, Ctr High Performance Comp, 1068 Xueyuan Rd, Shenzhen 518055, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Coll Comp Sci & Control Engn, Shenzhen, Peoples R China 5.Natl Res & Innovat Agcy, Res Ctr Artificial Intelligence & Cybersecur, Bandung, Indonesia
推荐引用方式 GB/T 7714	Niyogisubizo, Jovial,Zhao, Keliang,Meng, Jintao,et al. Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2024:13.
APA	Niyogisubizo, Jovial,Zhao, Keliang,Meng, Jintao,Pan, Yi,Didi, Rosiy Adi,&Wei, Yanjie.(2024).Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images.JOURNAL OF COMPUTATIONAL BIOLOGY,13.
MLA	Niyogisubizo, Jovial,et al."Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images".JOURNAL OF COMPUTATIONAL BIOLOGY (2024):13.