Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
No More Training: SAM’s Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation
Published in IEEE Access, 2024
This paper showcases SAM’s robustness and exceptional performance in medical image segmentation, even in the absence of direct training on these image types.
Recommended citation: Gutiérrez, J.D., Rodriguez-Echeverria, R., Delgado, E., Suero Rodrigo, M.Á., & Sánchez-Figueroa, F. (2024). 'No More Training: SAM's Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation.' IEEE Access.
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Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images
Published in IET Image Processing, 2024
This study evaluates the effectiveness of three loss functions—Asymmetric Unified Focal Loss (AUFL), Dice Similarity Coefficient Loss (DSCL), and Cross-Entropy (CE)—for segmenting COVID-19 lung infections in computed tomography images.
Recommended citation: Delgado, E., Rodriguez-Echeverria, R., Fernández-García, A.J., Gutiérrez, J.D., & Suero Rodrigo, M.Á. (2024). "Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images." IET Image Processing.
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Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt
Published in Algorithms, 2025
This paper introduces a novel approach leveraging SAM 2’s video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images.
Recommended citation: Gutiérrez, J.D., Delgado, E., Breuer, C., Conejero, J.M., & Rodriguez-Echeverria, R. (2025). "Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt." Algorithms, 18(4), 227.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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