Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Image segmentation plays a vital role in the medical field by isolating
organs or regions of interest from surrounding areas. Traditionally,
segmentation models are trained on a specific organ or a disease, limiting
their ability to handle other organs and diseases. At present, few advanced
models can perform multi-organ or multi-disease segmentation, offering greater
flexibility. Also, recently, prompt-based image segmentation has gained
attention as a more flexible approach. It allows models to segment areas based
on user-provided prompts. Despite these advances, there has been no dedicated
work on prompt-based interactive multi-organ and multi-disease segmentation,
especially for Chest X-rays. This work presents two main contributions: first,
generating doodle prompts by medical experts of a collection of datasets from
multiple sources with 23 classes, including 6 organs and 17 diseases,
specifically designed for prompt-based Chest X-ray segmentation. Second, we
introduce Prompt2SegCXR, a lightweight model for accurately segmenting multiple
organs and diseases from Chest X-rays. The model incorporates multi-stage
feature fusion, enabling it to combine features from various network layers for
better spatial and semantic understanding, enhancing segmentation accuracy.
Compared to existing pre-trained models for prompt-based image segmentation,
our model scores well, providing a reliable solution for segmenting Chest
X-rays based on user prompts.
