{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:13:20Z","timestamp":1772907200575,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"42","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["UL1TR002378"],"award-info":[{"award-number":["UL1TR002378"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM139967"],"award-info":[{"award-number":["R01GM139967"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Segmenting lung regions in ICU Chest X-rays (CXR\u2019s) is vital for diagnosing lung-related disorders, but existing methods require extensive annotations or training on large datasets. We present LuGSAM, a novel framework that integrates text prompts with the Segment Anything Model (SAM) for segmentation tasks, enhancing precision and adaptability in clinical settings. Our approach combines Grounding DINO, a zero-shot object detector using textual prompts (e.g., \"right lobe\"), and Meta AI\u2019s SAM. Grounding DINO generates bounding boxes based on word-level prompts. These bounding boxes serve as an input to SAM, to generate precise segmentation masks. To further improve accuracy, we propose an iterative bounding box adjustment algorithm that refines object detections through multiple iterations. The Vision Transformer huge (Vit-h) variant of SAM achieved the highest overlap score (IoU = 0.95) for right lung segmentation. Grounding DINO demonstrated high detection accuracy for prompts like \u201cright lung\u201d with a confidence score of 0.58. The Binarized Predicted IoU (BPIoU) metric showed significant improvements in segmentation quality, making this framework a promising tool for clinical applications.<\/jats:p>","DOI":"10.1007\/s11042-025-21094-5","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T09:35:26Z","timestamp":1758101726000},"page":"50119-50149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LuGSAM: a novel framework for integrating text prompts to Segment Anything Model (SAM) for segmentation tasks of ICU chest x-rays"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-333X","authenticated-orcid":false,"given":"Dhanush Babu","family":"Ramesh","sequence":"first","affiliation":[]},{"given":"Rishika Iytha","family":"Sridhar","sequence":"additional","affiliation":[]},{"given":"Pulakesh","family":"Upadhyaya","sequence":"additional","affiliation":[]},{"given":"Rishikesan","family":"Kamaleswaran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"21094_CR1","unstructured":"Tang L, Xiao H, Li B (2023) Can SAM Segment Anything? 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European Radiology Experimental 4:50. https:\/\/eurradiolexp.springeropen.com\/articles\/10.1186\/s41747-020-00173-2","DOI":"10.1186\/s41747-020-00173-2"},{"key":"21094_CR7","doi-asserted-by":"crossref","unstructured":"Iytha\u00a0Sridhar R, Kamaleswaran R (2023) Lung Segment Anything Model (LuSAM): A Prompt-integrated Framework for Automated Lung Segmentation on ICU Chest X-Ray Images. https:\/\/www.techrxiv.org\/articles\/preprint\/Lung_Segment_Anything_Model_LuSAM_A_Prompt-integrated_Framework_for_Automated_Lung_Segmentation_on_ICU_Chest_X-Ray_Images\/22788959\/1","DOI":"10.36227\/techrxiv.22788959.v1"},{"key":"21094_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Y, Jiao R (2023) Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey. arXiv:2305.03678 [cs, eess]","DOI":"10.2139\/ssrn.4495221"},{"key":"21094_CR9","doi-asserted-by":"crossref","unstructured":"Mazurowski MA et al (2023) Segment anything model for medical image analysis: An experimental study. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841523001780","DOI":"10.1016\/j.media.2023.102918"},{"key":"21094_CR10","doi-asserted-by":"crossref","unstructured":"Zhang K, Liu D (2023) Customized Segment Anything Model for Medical Image Segmentation. arXiv:2304.13785 [cs]","DOI":"10.2139\/ssrn.4495221"},{"key":"21094_CR11","doi-asserted-by":"crossref","unstructured":"Shah AA, Malik HAM, Muhammad A, Alourani A, Butt ZA (2023) Deep learning ensemble 2D CNN approach towards the detection of lung cancer. 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