SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Qing Xu Wenwei Kuang Zeyu Zhang Xueyao Bao Haoran Chen Wenting Duan
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.