# CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation Source: [CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:50:52.513Z Authors: Weibin Liao, Xuhong Li, Qingzhong Wang, Yanwu Xu, Zhaozheng Yin, Haoyi Xiong Published: 2023-10-06T02:35:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()