2023-06-04-bai23a.md

title	booktitle	year	volume	series	month	publisher	pdf	url	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
YUSEG: Yolo and Unet is all you need for cell instance segmentation	Proceedings of the NeurIPS Challenge on Cell Segmentation in Muliti-modality Microscopy Images	2022	212	Proceedings of Machine Learning Research	0	PMLR	https://proceedings.mlr.press/v212/bai23a/bai23a.pdf	https://proceedings.mlr.press/212/bai22.html	Cell instance segmentation, which identifies each specific cell area within a mi- croscope image, is helpful for cell analysis. Because of the high computational cost brought on by the large number of objects in the scene, mainstream instance segmentation techniques require much time and computational resources. In this paper, we proposed a two-stage method in which the first stage detects the bounding boxes of cells, and the second stage is segmentation in the detected bounding boxes. This method reduces inference time by more than 30% on images that image size is larger than 1024 pixels by 1024 pixels compared to the mainstream instance segmentation method while maintaining reasonable accuracy without using any external data.	inproceedings	2640-3498	bai23a	YUSEG: Yolo and Unet is all you need for cell instance segmentation	1	15	1-15	1	false	Ma, Jun and Xie, Ronald and Gupta, Anubha and Guilherme de Almeida, Jos\'e and Bader, Gary D. and Wang, Bo	given family Jun Ma given family Ronald Xie given family Anubha Gupta given family prefix José Almeida Guilherme de given family Gary D. Bader given family Bo Wang	Bai, Bizhe and Tian, Jie and Luo, Sicong and Wang, Tao and Lyu, Sisuo	given family Bizhe Bai given family Jie Tian given family Sicong Luo given family Tao Wang given family Sisuo Lyu	2023-06-04		Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images	inproceedings	date-parts 2023 6 4