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HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation.

Xiaona Yang¹, Bo Ding¹, Jian Qin¹

¹Harbin University of Science and Technology, School of Computer Science and Technology, Harbin, 150080, China.

Computers in Biology and Medicine

|February 22, 2024

Summary

This summary is machine-generated.

This study introduces HVS-Unsup, an unsupervised method for cervical cell instance segmentation that mimics human vision. It significantly reduces the need for labeled data in cervical cancer diagnosis.

Keywords:

Cervical cell segmentation Deep learning Human visual simulation Prior knowledge Unsupervised instance segmentation

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Instance segmentation is crucial for automated cervical cancer diagnosis.
Deep learning methods require extensive labeled data, posing resource challenges.

Purpose of the Study:

To develop an unsupervised instance segmentation method for cervical cells.
To reduce reliance on manually labeled datasets in medical image analysis.

Main Methods:

Human visual simulation (HVS-Unsup) incorporating prior cervical cell knowledge.
Generation of pseudo-labels to transform unsupervised to supervised tasks.
Nucleus Enhanced Module (NEM), Mask-Assisted Segmentation (MAS), Category-Wise droploss (CW-droploss), and iterative self-training.

Main Results:

HVS-Unsup effectively segments cervical cells without extensive labeled data.
NEM and MAS modules address challenges like cell overlap and visual indistinguishability.
CW-droploss improves segmentation in low-contrast images, reducing omissions.

Conclusions:

HVS-Unsup offers a viable unsupervised alternative for cervical cell instance segmentation.
The method demonstrates superior performance compared to existing unsupervised techniques on multiple datasets.
This approach has the potential to streamline cervical cancer diagnosis workflows.