Image segmentation plays an essential role in nuclei image analysis. Recently, the segment anything model has made a significant breakthrough in such tasks. However, the current model exists two major issues for cell segmentation: (1) the image encoder of the segment anything model involves a large number of parameters. Retraining or even fine-tuning the model still requires expensive computational resources. (2) in point prompt mode, points are sampled from the center of the ground truth and more than one set of points is expected to achieve reliable performance, which is not efficient for practical applications. In this paper, a single-point prompt network is proposed for nuclei image segmentation, called SPPNet. We replace the original image encoder with a lightweight vision transformer. Also, an effective convolutional block is added in parallel to extract the low-level semantic information from the image and compensate for the performance degradation due to the small image encoder. We propose a new point-sampling method based on the Gaussian kernel. The proposed model is evaluated on the MoNuSeg-2018 dataset. The result demonstrated that SPPNet outperforms existing U-shape architectures and shows faster convergence in training. Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost. Particularly, only one set of points is required in both the training and inference phases, which is more reasonable for clinical applications. The code for our work and more technical details can be found at https://github.com/xq141839/SPPNet.

翻译：图像分割在细胞核图像分析中扮演着重要角色。近年来，"分割一切模型"在此类任务中取得了显著突破。然而，当前模型在细胞分割中存在两大问题：（1）"分割一切模型"的图像编码器包含大量参数，重新训练甚至微调模型仍需昂贵的计算资源；（2）在点提示模式下，需从真实标签中心采样点，且需使用多组点才能获得可靠性能，这在实际应用中效率低下。本文提出了一种用于细胞核图像分割的单点提示网络，称为SPPNet。我们采用轻量级视觉Transformer替代原始图像编码器，同时并行添加高效的卷积模块以提取图像中的低级语义信息，从而补偿因小型化图像编码器导致的性能下降。此外，我们提出了一种基于高斯核的新型点采样方法。在MoNuSeg-2018数据集上的评估结果表明，SPPNet优于现有U型架构，且训练收敛速度更快。与"分割一切模型"相比，SPPNet的推理速度提升约20倍，参数量和计算成本仅为前者的1/70。尤为重要的是，训练和推理阶段仅需一组单点，这使得该网络更适用于临床应用。本工作的代码及更多技术细节详见https://github.com/xq141839/SPPNet。