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HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation.
Yang, Xiaona; Ding, Bo; Qin, Jian; Guo, Luyao; Zhao, Jing; He, Yongjun.
Afiliación
  • Yang X; Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China.
  • Ding B; Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China.
  • Qin J; Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China.
  • Guo L; Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China.
  • Zhao J; Northeast Forestry University, School of Mechanical and Electrical Engineering, Harbin, 150040, China.
  • He Y; Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, China. Electronic address: heyongjun@hit.edu.cn.
Comput Biol Med ; 171: 108147, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38387385
ABSTRACT
Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Cuello Uterino Límite: Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Cuello Uterino Límite: Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos