Knowledge Commons of Institute of Automation,CAS
SCTS: Instance Segmentation of Single Cells Using a Transformer-Based Semantic-Aware Model and Space-Filling Augmentation | |
Zhou,Yating1,2; Li,wenjing1,2; Yang,ge1,2 | |
2023-01 | |
会议名称 | IEEE/CVF Winter Conference on Applications of Computer Vision |
会议日期 | 2023-1-3 |
会议地点 | Waikoloa, Hawaii |
摘要 | Instance segmentation of single cells from microscopy images is critical to quantitative analysis of their spatial and morphological features for many important biomedical applications, such as disease diagnosis and drug screening. However, the high densities, tight contacts, and weak boundaries of the cells pose substantial technical challenges. To overcome these challenges, we have developed a new instance segmentation model, which we refer to as single-cell Transformer segmenter (SCTS). It utilizes a Swin Transformer as its backbone, combining the global modeling capabilities of a Transformer and the local modeling capabilities of a convolutional neural network (CNN) to ensure model adaptability to different cell sizes, shapes, and textures. It also embeds a three-class (background, cell interior, and cell boundary) semantic segmentation branch to classify pixels and to provide semantic features for downstream tasks. The prediction of boundary semantics improves boundary awareness, and the differentiation between foreground and background semantics improves segmentation integrity in regions with weak signals. To reduce the need for annotated training data, we have developed an augmentation strategy that randomly fills instances of single cells into open spaces of training images. Experiments show that our model outperforms several state-of-the-art models on the LIVECell dataset and an in-house dataset. The code and dataset of this work are openly accessible at https://github.com/cbmi-group/SCTS. |
关键词 | Microscopy Cell Images Instance Segmentation Cell Adhesion Data Scarcity Transformer |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 生物进化与仿生计算 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51880 |
专题 | 多模态人工智能系统全国重点实验室_计算生物学与机器智能 多模态人工智能系统全国重点实验室 |
通讯作者 | Yang,ge |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhou,Yating,Li,wenjing,Yang,ge. SCTS: Instance Segmentation of Single Cells Using a Transformer-Based Semantic-Aware Model and Space-Filling Augmentation[C],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Zhou_SCTS_Instance_S(7001KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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