Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, including the recently emerging deep learning-based methods, still show limited generality under the multi-modality environment. Weakly Supervised Cell Segmentation in Multi-modality High-Resolution Microscopy Images was hosted at NeurIPS 2022 to tackle this problem. We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality in this challenge. MEDIAR harmonizes data-centric and model-centric approaches as the learning and inference strategies, achieving a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source: https://github.com/Lee-Gihun/MEDIAR
翻译:细胞分离是计算生物学分析的一项基本任务。确定细胞分解是各种下游生物医学研究的第一步。然而,许多细胞分解算法,包括最近出现的深层次学习方法,在多模式环境中仍然表现出有限的一般性。在多模式环境中,对多模式高分辨率显微镜图像中细胞分解的监管不力,在NeurIPS 2022年托管了多模式高分辨率显微镜图像,以解决这一问题。我们提议,在这项挑战中,在多模式下为细胞分解提供一个整体管道,即MEDIAR。 MEDIAR统一了以数据为中心和以模型为中心的方法,作为学习和推断战略,在验证阶段达到0.9067 F1核心,同时满足时间预算。为了便利随后的研究,我们提供源代码和经过培训的模型,作为开放源码:https://github.com/Lee-Gihun/MEDIAR。