  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.
