准确分割细胞病理图像中的白细胞对于白血病的评估至关重要。然而，在临床实践中，分割是困难的。由于需要处理非常大量的细胞病理图像，诊断变得繁琐和耗时，并且诊断准确性与专家的经验、疲劳和情绪等密切相关。此外，完全自动化的白细胞分割有几个挑战。细胞变形、模糊的细胞边界、细胞色彩差异、细胞重叠或粘附等都存在。提出的方法通过利用Ghost模块特征重用来重构轻量级骨干网络，提高了网络的特征表示能力，同时减少了参数和计算冗余。此外，基于特征金字塔网络设计了双流特征融合网络（DFFN），以增强详细信息的获取。此外，开发了双域注意模块（DDAM），可以同时从频域和空间域提取全局特征，以提高细胞分割性能。ALL-IDB和BCCD数据集上的实验结果表明，我们的方法在平均精度（AP）方面超过了现有的实例分割网络，如Mask R-CNN、PointRend、MS R-CNN、SOLOv2和YOLACT，达到了87.41％，同时显著减少了参数和计算成本。我们的方法在参数数量和FLOPs方面明显优于当前最先进的单阶段方法，并且在所有比较方法中性能最佳。然而，我们的方法的性能仍然低于两阶段的实例分割算法。在未来的工作中，如何设计更轻量级的网络模型，同时确保良好的准确性，将成为一个重要的问题。版权所有 © 2023 Luo, Wang, Zhao, Guan, Shi, Fu 和 Jiang。

Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.Copyright © 2023 Luo, Wang, Zhao, Guan, Shi, Fu and Jiang.