VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation

Xiaochen Cai, Hengxing Cai, Kele Xu, Wei-Wei Tu, Wu-Jun Li
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-13, 2023.

Abstract

Cell instance segmentation is a fundamental task in analyzing microscopy images, with applications in computer-aided biomedical research. In recent years, deep learning techniques have been widely used in this field. However, existing methods exhibit inadequate generalization ability towards multi-modal cellular images and require a considerable amount of manually labeled data for training. To overcome these limitations, we present VSM, a versatile semi-supervised model for multi-modal cell instance segmentation. Our method delivers high accuracy and efficiency, as verified through comprehensive experiments. Additionally, VSM achieved a top-five ranking in the Weakly Supervised Cell Segmentation category of the multi-modal High-Resolution Microscopy competition.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-cai23a, title = {VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation}, author = {Cai, Xiaochen and Cai, Hengxing and Xu, Kele and Tu, Wei-Wei and Li, Wu-Jun}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--13}, year = {2023}, editor = {Ma, Jun and Xie, Ronald and Gupta, Anubha and Guilherme de Almeida, José and Bader, Gary D. and Wang, Bo}, volume = {212}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v212/cai23a/cai23a.pdf}, url = {https://proceedings.mlr.press/v212/cai23a.html}, abstract = {Cell instance segmentation is a fundamental task in analyzing microscopy images, with applications in computer-aided biomedical research. In recent years, deep learning techniques have been widely used in this field. However, existing methods exhibit inadequate generalization ability towards multi-modal cellular images and require a considerable amount of manually labeled data for training. To overcome these limitations, we present VSM, a versatile semi-supervised model for multi-modal cell instance segmentation. Our method delivers high accuracy and efficiency, as verified through comprehensive experiments. Additionally, VSM achieved a top-five ranking in the Weakly Supervised Cell Segmentation category of the multi-modal High-Resolution Microscopy competition.} }
Endnote
%0 Conference Paper %T VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation %A Xiaochen Cai %A Hengxing Cai %A Kele Xu %A Wei-Wei Tu %A Wu-Jun Li %B Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images %C Proceedings of Machine Learning Research %D 2023 %E Jun Ma %E Ronald Xie %E Anubha Gupta %E José Guilherme de Almeida %E Gary D. Bader %E Bo Wang %F pmlr-v212-cai23a %I PMLR %P 1--13 %U https://proceedings.mlr.press/v212/cai23a.html %V 212 %X Cell instance segmentation is a fundamental task in analyzing microscopy images, with applications in computer-aided biomedical research. In recent years, deep learning techniques have been widely used in this field. However, existing methods exhibit inadequate generalization ability towards multi-modal cellular images and require a considerable amount of manually labeled data for training. To overcome these limitations, we present VSM, a versatile semi-supervised model for multi-modal cell instance segmentation. Our method delivers high accuracy and efficiency, as verified through comprehensive experiments. Additionally, VSM achieved a top-five ranking in the Weakly Supervised Cell Segmentation category of the multi-modal High-Resolution Microscopy competition.
APA
Cai, X., Cai, H., Xu, K., Tu, W. & Li, W.. (2023). VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-13 Available from https://proceedings.mlr.press/v212/cai23a.html.

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