Re-Unet:Multi-Modality Cell Segmentation based on nnU-Net Pipeline

Haotian Lu, Jinghao Feng, Zelin Peng, Wei Shen
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-9, 2023.

Abstract

Cell segmentation is an important initial task in medical image analysis, and in recent years, data-driven deep learning methods have made groundbreaking achievements in this field. In this challenge, a multi-modal and partially labeled dataset is provided. In this paper, we propose a multi-modality cell segmentation framework called Re-Unet, which is based on the nnU-Net pipeline and an iterative self-training method. Re-Unet enriches the original data and fully considers the information of cell intervals while making full use of the semi-supervised data. Our proposed method achieves a mean F1 score of 0.6101 on the tuning set and a F1 score of 0.4492 on the testing set.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-lu23a, title = {Re-Unet:Multi-Modality Cell Segmentation based on nnU-Net Pipeline}, author = {Lu, Haotian and Feng, Jinghao and Peng, Zelin and Shen, Wei}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--9}, 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/lu23a/lu23a.pdf}, url = {https://proceedings.mlr.press/v212/lu23a.html}, abstract = {Cell segmentation is an important initial task in medical image analysis, and in recent years, data-driven deep learning methods have made groundbreaking achievements in this field. In this challenge, a multi-modal and partially labeled dataset is provided. In this paper, we propose a multi-modality cell segmentation framework called Re-Unet, which is based on the nnU-Net pipeline and an iterative self-training method. Re-Unet enriches the original data and fully considers the information of cell intervals while making full use of the semi-supervised data. Our proposed method achieves a mean F1 score of 0.6101 on the tuning set and a F1 score of 0.4492 on the testing set.} }
Endnote
%0 Conference Paper %T Re-Unet:Multi-Modality Cell Segmentation based on nnU-Net Pipeline %A Haotian Lu %A Jinghao Feng %A Zelin Peng %A Wei Shen %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-lu23a %I PMLR %P 1--9 %U https://proceedings.mlr.press/v212/lu23a.html %V 212 %X Cell segmentation is an important initial task in medical image analysis, and in recent years, data-driven deep learning methods have made groundbreaking achievements in this field. In this challenge, a multi-modal and partially labeled dataset is provided. In this paper, we propose a multi-modality cell segmentation framework called Re-Unet, which is based on the nnU-Net pipeline and an iterative self-training method. Re-Unet enriches the original data and fully considers the information of cell intervals while making full use of the semi-supervised data. Our proposed method achieves a mean F1 score of 0.6101 on the tuning set and a F1 score of 0.4492 on the testing set.
APA
Lu, H., Feng, J., Peng, Z. & Shen, W.. (2023). Re-Unet:Multi-Modality Cell Segmentation based on nnU-Net Pipeline. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-9 Available from https://proceedings.mlr.press/v212/lu23a.html.

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