Multiscale Progressive Text Prompt Network for Medical Image Segmentation

TL;DR

This paper introduces a multiscale progressive text prompt network that leverages text priors and contrastive learning to improve medical image segmentation accuracy while reducing annotation costs, demonstrating robustness across datasets.

Contribution

The proposed model uniquely integrates progressive text prompts with multiscale feature fusion and contrastive pretraining for enhanced medical image segmentation.

Findings

Achieves high segmentation accuracy with less labeled data.

Performs well on both medical and natural images.

Demonstrates robustness and reliability across datasets.

Abstract

The accurate segmentation of medical images is a crucial step in obtaining reliable morphological statistics. However, training a deep neural network for this task requires a large amount of labeled data to ensure high-accuracy results. To address this issue, we propose using progressive text prompts as prior knowledge to guide the segmentation process. Our model consists of two stages. In the first stage, we perform contrastive learning on natural images to pretrain a powerful prior prompt encoder (PPE). This PPE leverages text prior prompts to generate multimodality features. In the second stage, medical image and text prior prompts are sent into the PPE inherited from the first stage to achieve the downstream medical image segmentation task. A multiscale feature fusion block (MSFF) combines the features from the PPE to produce multiscale multimodality features. These two progressive features not only bridge the semantic gap but also improve prediction accuracy. Finally, an UpAttention block refines the predicted results by merging the image and text features. This design provides a simple and accurate way to leverage multiscale progressive text prior prompts for medical image segmentation. Compared with using only images, our model achieves high-quality results with low data annotation costs. Moreover, our model not only has excellent reliability and validity on medical images but also performs well on natural images. The experimental results on different image datasets demonstrate that our model is effective and robust for image segmentation.