Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at https://github.com/funkelab/cellulus.

翻译：对于许多生物医学应用而言，显微图像中的对象分割是必需的。我们提出对象中心嵌入（OCEs），该方法对图像块进行嵌入，使得从同一对象裁剪出的图像块之间的空间偏移得以保留。这些学习到的嵌入可用于勾勒单个对象的轮廓，从而获得实例分割。本文从理论上证明，在显微图像常见的假设条件下，OCEs可通过一项预测图像块间空间偏移的自监督任务来学习。由此形成一种无监督细胞实例分割方法，并在九个多样化的大规模显微数据集上进行了评估。与六个数据集上的最先进基线方法相比，我们的方法所得分割结果显著提升，在其余三个数据集上表现相当。若存在真实标注，我们的方法可作为监督训练的出色起点，将所需真实标注量减少一个数量级，从而大幅提升方法的实际应用性。源代码已开源至 https://github.com/funkelab/cellulus。