Cap2Seg: leveraging caption generation for enhanced segmentation of COVID-19 medical images

Abstract

Incorporating medical text annotations compensates for the quality deficiencies of image data, effectively overcoming the limitations of medical image segmentation. Many existing approaches achieve high-quality segmentation results by integrating text into the image modality. However, these approaches require matched image-text pairs during inference to maintain their performance, and the absence of corresponding text annotations results in degraded model performance. Additionally, these methods often assume that the input text annotations are ideal, overlooking the impact of poor-quality text on model performance in practical scenarios. To address these issues, we propose a novel generative medical image segmentation model, Cap2Seg (Leveraging Caption Generation for Enhanced Segmentation of COVID-19 Medical Images). Cap2Seg not only segments lesion areas but also generates related medical text descriptions, guiding the segmentation process. This design enables the model to perform optimal segmentation without requiring text input during inference. To mitigate the impact of inaccurate text on model performance, we consider the consistency between generated textual features and visual features and introduce the Scale-aware Textual Attention Module (SATaM), which reduces the model’s dependency on irrelevant or misleading text information. Subsequently, we design a word-pixel fusion decoding mechanism that effectively integrates textual features into visual features, ensuring that the text information effectively supplements and enhances the image segmentation task. Extensive experiments on two public datasets, MosMedData+ and QaTa-COV19, demonstrate that our method outperforms the current state-of-the-art models under the same conditions. Additionally, ablation studies have been conducted to demonstrate the effectiveness of each proposed module. The code is available at https://github.com/AllenZzzzzzzz/Cap2Seg.