Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays
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arXiv
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| Format: | Preprint |
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2025
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| author | Zami, Abduz Sobhan, Shadman Hossain, Rounaq Sorker, Md. Sawran Ahmed, Mohiuddin Hossain, Md. Redwan |
| author_facet | Zami, Abduz Sobhan, Shadman Hossain, Rounaq Sorker, Md. Sawran Ahmed, Mohiuddin Hossain, Md. Redwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays Zami, Abduz Sobhan, Shadman Hossain, Rounaq Sorker, Md. Sawran Ahmed, Mohiuddin Hossain, Md. Redwan Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.00673 |