Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images

Wei Lou, Xinyi Yu, Chenyu Liu, Xiang Wan, Guanbin Li, Siqi Liu, Haofeng Li
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR 212:1-10, 2023.

Abstract

Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v212-lou23a, title = {Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images}, author = {Lou, Wei and Yu, Xinyi and Liu, Chenyu and Wan, Xiang and Li, Guanbin and Liu, Siqi and Li, Haofeng}, booktitle = {Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images}, pages = {1--10}, year = {2023}, editor = {}, volume = {212}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v212/lou23a/lou23a.pdf}, url = {https://proceedings.mlr.press/v212/lou23a.html}, abstract = {Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance.} }
Endnote
%0 Conference Paper %T Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images %A Wei Lou %A Xinyi Yu %A Chenyu Liu %A Xiang Wan %A Guanbin Li %A Siqi Liu %A Haofeng Li %B Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images %C Proceedings of Machine Learning Research %D 2023 %E %F pmlr-v212-lou23a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v212/lou23a.html %V 212 %X Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance.
APA
Lou, W., Yu, X., Liu, C., Wan, X., Li, G., Liu, S. & Li, H.. (2023). Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images. Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, in Proceedings of Machine Learning Research 212:1-10 Available from https://proceedings.mlr.press/v212/lou23a.html.

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