LIVECell dataset

Light microscopy is a cheap, accessible, non-invasive modality that when combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells enables exploration of complex biological questions, but this requires sophisticated imaging processing pipelines due to the low contrast and high object density. <br><br>Deep learning-based methods are considered state-of-the-art for most computer vision problems but require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. <br><br>To address this gap we present LIVECell, a high-quality, manually annotated and expert-validated dataset that is the largest of its kind to date, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its utility, we provide convolutional neural network-based models trained and evaluated on LIVECell.

光学显微镜（Light Microscopy）是一种成本低廉、易于获取且无创的成像模态，将其与成熟的二维细胞培养方案相结合，可实现高通量定量成像以研究生物学现象。精准的单细胞分割可为复杂生物学问题的探索提供支撑，但由于图像对比度较低且目标细胞密度较高，该任务需要借助复杂的图像处理流程方可完成。
基于深度学习的方法在多数计算机视觉任务中被视为当前最优方案，但此类方法需要海量带标注的训练数据，而在无标记细胞成像（label-free cellular imaging）领域，目前尚无合适的相关数据集可供使用。
为填补这一研究空白，本研究推出LIVECell数据集——这是目前同类数据集中规模最大的优质资源，经人工标注与专家验证，其包含来自多种细胞形态与培养密度的超过160万个细胞。为进一步验证该数据集的应用价值，我们还提供了基于卷积神经网络（Convolutional Neural Network）、并在LIVECell上完成训练与评估的模型。

搜集汇总

背景与挑战

背景概述

LIVECell是一个高质量、手动标注的细胞成像数据集，包含超过160万个细胞，涵盖多种细胞形态和培养密度，用于支持计算机视觉和深度学习研究。数据集还提供了预训练的卷积神经网络模型，以展示其实际应用价值。