While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model. To lower the cost of annotation, this work considers the problem of pre-training DNN models for few-shot cell segmentation, where massive unlabeled cell images are available but only a small proportion is annotated. Hereby, we propose Cross-domain Unsupervised Pre-training, namely CUPre, transferring the capability of object detection and instance segmentation for common visual objects (learned from COCO) to the visual domain of cells using unlabeled images. Given a standard COCO pre-trained network with backbone, neck, and head modules, CUPre adopts an alternate multi-task pre-training (AMT2) procedure with two sub-tasks -- in every iteration of pre-training, AMT2 first trains the backbone with cell images from multiple cell datasets via unsupervised momentum contrastive learning (MoCo) [3], and then trains the whole model with vanilla COCO datasets via instance segmentation. After pre-training, CUPre fine-tunes the whole model on the cell segmentation task using a few annotated images. We carry out extensive experiments to evaluate CUPre using LIVECell [2] and BBBC038 [4] datasets in few-shot instance segmentation settings. The experiment shows that CUPre can outperform existing pre-training methods, achieving the highest average precision (AP) for few-shot cell segmentation and detection.

翻译：尽管在目标检测任务（如COCO[1]）上的预训练能显著提升细胞分割性能，但仍需在大量精细标注的细胞图像[2]上（为每张图像中的每个细胞标注边界框、掩膜和细胞类型）对预训练模型进行微调。为降低标注成本，本研究考虑在少样本细胞分割场景下预训练深度神经网络模型——该场景中存在大量未标注细胞图像，但仅少量样本被标注。为此，我们提出跨域无监督预训练方法CUPre，利用未标注图像将通用视觉目标（从COCO学习）的检测与实例分割能力迁移至细胞视觉域。给定标准COCO预训练网络（包含主干、颈部与头部模块），CUPre采用交替式多任务预训练（AMT2）流程：在每次预训练迭代中，首先通过无监督动量对比学习（MoCo）[3]用多数据集细胞图像训练主干网络，随后用标准COCO数据集对完整模型进行实例分割训练。预训练完成后，CUPre通过少量标注图像对完整模型进行细胞分割微调。我们在LIVECell[2]和BBBC038[4]数据集上开展少样本实例分割实验，结果表明CUPre优于现有预训练方法，在少样本细胞分割与检测任务中取得最高平均精度（AP）。