{"id":{"repo_id":"rosario","oai_identifier":"oai:repository.urosario.edu.co:10336/48082"},"canonical_url":"https://search.dev.ndltd.org/etd/rosario/oai:repository.urosario.edu.co:10336/48082","repository":{"repo_id":"rosario","name":"Universidad del Rosario","base_url":"https://repository.urosario.edu.co/oai/request"},"display":{"title":"Comparación de algoritmos de segmentación heurística de imágenes de angiografía coronaria utilizando técnicas no supervisadas","abstract":"El presente trabajo aborda el problema de la segmentación no supervisada de vasos coronarios en imágenes de angiografía, un desafío debido al ruido, los artefactos y la complejidad de las estructuras vasculares. Se comparan dos enfoques de segmentación, W-Net y Segment Anything Model (SAM), y se propone un modelo híbrido que combina las capacidades especializadas de W-Net con la precisión de segmentación de SAM. La estrategia desarrollada utiliza un mapa generado por W-Net para guiar la selección de las segmentaciones producidas por SAM. Los resultados muestran que, aunque el modelo híbrido presenta una menor similitud con las pseudo-etiquetas de entrenamiento, genera segmentaciones visualmente más coherentes y anatómicamente más precisas, evidenciando su potencial para mejorar la segmentación no supervisada de imágenes de angiografía coronaria.","abstract_html":"El presente trabajo aborda el problema de la segmentación no supervisada de vasos coronarios en imágenes de angiografía, un desafío debido al ruido, los artefactos y la complejidad de las estructuras vasculares. Se comparan dos enfoques de segmentación, W-Net y Segment Anything Model (SAM), y se propone un modelo híbrido que combina las capacidades especializadas de W-Net con la precisión de segmentación de SAM. La estrategia desarrollada utiliza un mapa generado por W-Net para guiar la selección de las segmentaciones producidas por SAM. Los resultados muestran que, aunque el modelo híbrido presenta una menor similitud con las pseudo-etiquetas de entrenamiento, genera segmentaciones visualmente más coherentes y anatómicamente más precisas, evidenciando su potencial para mejorar la segmentación no supervisada de imágenes de angiografía coronaria.","abstract_has_math":false,"creators":["Ramírez Millán, Nicolás"],"institution":"Universidad del Rosario","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-06-12","date_published":"2026-06-12","updated_at":"2026-07-27T20:46:27Z","subjects":["Segmentación No Supervisada","Angiografia coronaria","Deep learning","W-Net","Segment Anything Mode","Unsupervised segmentation","Coronary angiography","Segment Anything Model"],"languages":["spa"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://repository.urosario.edu.co/handle/10336/48082","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ramírez Millán, Nicolás"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-06-12","2026-07-21T23:15:50Z"]},{"key":"dc:publisher","label":"Institution","values":["Universidad del Rosario","Escuela Colombiana de Ingeniería Julio Garavito","Escuela de Medicina y Ciencias de la Salud","Maestría en Ingeniería Biomédica"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis","info:eu-repo/semantics/acceptedVersion"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Segmentación No Supervisada","Angiografia coronaria","Deep learning","W-Net","Segment Anything Mode","Unsupervised segmentation","Coronary angiography","Segment Anything Model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["spa"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.urosario.edu.co/handle/10336/48082"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["El presente trabajo aborda el problema de la segmentación no supervisada de vasos coronarios en imágenes de angiografía, un desafío debido al ruido, los artefactos y la complejidad de las estructuras vasculares. Se comparan dos enfoques de segmentación, W-Net y Segment Anything Model (SAM), y se propone un modelo híbrido que combina las capacidades especializadas de W-Net con la precisión de segmentación de SAM. La estrategia desarrollada utiliza un mapa generado por W-Net para guiar la selección de las segmentaciones producidas por SAM. Los resultados muestran que, aunque el modelo híbrido presenta una menor similitud con las pseudo-etiquetas de entrenamiento, genera segmentaciones visualmente más coherentes y anatómicamente más precisas, evidenciando su potencial para mejorar la segmentación no supervisada de imágenes de angiografía coronaria.","This paper addresses the problem of unsupervised segmentation of coronary vessels in angiography images, a challenge posed by noise, artifacts, and the complexity of vascular structures. Two segmentation approaches, W-Net and the Segment Anything Model (SAM), are compared, and a hybrid model is proposed that combines the specialized capabilities of W-Net with the segmentation accuracy of SAM. The developed strategy uses a map generated by W-Net to guide the selection of segmentations produced by SAM. The results show that, although the hybrid model exhibits less similarity to the training pseudo-labels, it generates visually more coherent and anatomically more accurate segmentations, demonstrating its potential to improve unsupervised segmentation of coronary angiography images."]},{"key":"dc:format","label":"Dc Format","values":["70 pp","application/pdf"]},{"key":"dc:source","label":"Dc Source","values":["J. El-Taraboulsi, C. P. Cabrera, C. Roney, and N. Aung, “Deep neural network architectures for cardiac image segmentation,” Artificial Intelligence in the Life Sciences, vol. 4, p. 100083, Dec. 2023, doi: 10.1016/j.ailsci.2023.100083.","C. Bian et al., “Uncertainty-aware domain alignment for anatomical structure segmentation,” Med. Image Anal., vol. 64, p. 101732, Aug. 2020, doi: 10.1016/j.media.2020.101732","X. Xia and B. Kulis, “W-Net: A Deep Model for Fully Unsupervised Image Segmentation,” Nov. 2017","A. Medellin, D. Grabowsky, D. Mikulski, and R. Langari, “Sam-Sam - A Novel Approach to Hyperspectral Image Semantic Segmentation,” in 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, Oct. 2023, pp. 1–5. doi: 10.1109/WHISPERS61460.2023.10431253.","W. Chen et al., “Prostate Segmentation using 2D Bridged U-net,” in 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2019, pp. 1–7. doi: 10.1109/IJCNN.2019.8851908.","Y. Zhang, Z. Shen, and R. Jiao, “Segment anything model for medical image segmentation: Current applications and future directions,” Comput. Biol. Med., vol. 171, p. 108238, Mar. 2024, doi: 10.1016/j.compbiomed.2024.108238.","T.-Y. Hsia, E. A. Peck, and J. V. Conte, “Coronary Artery Disease,” in The Johns Hopkins Manual of Cardiac Surgical Care, Elsevier, 2008, pp. 61–93. doi: 10.1016/B978-0-323-01810-4.10003-3.","B. A. Stark et al., “Global, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990-2023,” JACC, Sep. 2025, doi: 10.1016/j.jacc.2025.08.015.","S. Lee, M. Lee, and M. Kang, “Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain,” Sensors, vol. 18, no. 4, p. 1019, Mar. 2018, doi: 10.3390/s18041019.","LUQMAN and FAKHRUD DIN, “A Hyper-Heuristic Based Strategy for Image Segmentation Using Multilevel Thresholding,” Journal of Quality Measurement and Analysis, vol. 21, no. 2, pp. 123–152, Jun. 2025, doi: 10.17576/jqma.2102.2025.10.","Y. Sato et al., “Tissue classification based on 3D local intensity structures for volume rendering,” IEEE Trans. Vis. Comput. Graph., vol. 6, no. 2, pp. 160–180, 2000, doi: 10.1109/2945.856997.","T. Jerman, F. Pernus, B. Likar, and Z. Spiclin, “Enhancement of Vascular Structures in <?Pub _newline ?> 3D and 2D Angiographic Images,” IEEE Trans. Med. Imaging, vol. 35, no. 9, pp. 2107–2118, Sep. 2016, doi: 10.1109/TMI.2016.2550102","D. Indra, T. Hasanuddin, R. Satra, and N. R. Wibowo, “Eggs Detection Using Otsu Thresholding Method,” in 2018 2nd East Indonesia Conference on Computer and 65 Information Technology (EIConCIT), IEEE, Nov. 2018, pp. 10–13. doi: 10.1109/EIConCIT.2018.8878517","Chen Yu, Chen Dian-ren, Li Yang, and Chen Lei, “Otsu’s thresholding method based on gray level-gradient two-dimensional histogram,” in 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), IEEE, Mar. 2010, pp. 282–285. doi: 10.1109/CAR.2010.5456687.","D. Shi, R. 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Tian, “RankSAM: Lightweight adapters and prompt generation in zero-shot semantic segmentation,” Neurocomputing, vol. 670, p. 132594, Mar. 2026, doi: 10.1016/j.neucom.2025.132594.","M. Marjani, M. Mahdianpari, D. J. Varon, and F. Mohammadimanesh, “The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data,” J. Environ. Manage., vol. 393, p. 127034, Oct. 2025, doi: 10.1016/j.jenvman.2025.127034","A. Carraro, M. Sozzi, and F. Marinello, “The Segment Anything Model (SAM) for accelerating the smart farming revolution,” Smart Agricultural Technology, vol. 6, p. 100367, Dec. 2023, doi: 10.1016/j.atech.2023.100367.","Y. Huang et al., “Segment anything model for medical images?,” Med. Image Anal., vol. 92, p. 103061, Feb. 2024, doi: 10.1016/j.media.2023.103061.","J. Wu et al., “Medical SAM adapter: Adapting segment anything model for medical image segmentation,” Med. 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Hanbury, “Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool,” BMC Med. Imaging, vol. 15, no. 1, p. 29, Dec. 2015, doi: 10.1186/s12880-015-0068-x.","S. Ghosal and P. Shah, “A deep-learning toolkit for visualization and interpretation of segmented medical images,” Cell Reports Methods, vol. 1, no. 7, p. 100107, Nov. 2021, doi: 10.1016/j.crmeth.2021.100107.","Z. Li, D. Huo, M. Meurer, M. Panesso, W.-G. Drossel, and T. Bergs, “Enhancing Tool Wear Segmentation with LoRA-SAM and Point Prompts,” Procedia CIRP, vol. 134, pp. 705–710, 2025, doi: 10.1016/j.procir.2025.02.184","instname:Universidad del Rosario","reponame:Repositorio Institucional EdocUR"]},{"key":"dc:title","label":"Title","values":["Comparación de algoritmos de segmentación heurística de imágenes de angiografía coronaria utilizando técnicas no supervisadas","Comparison of heuristic segmentation algorithms for coronary angiography images using unsupervised techniques"]}]}],"canonical_facts":{"dc:creator":["Ramírez Millán, Nicolás"],"dc:date":["2026-06-12","2026-07-21T23:15:50Z"],"dc:description":["El presente trabajo aborda el problema de la segmentación no supervisada de vasos coronarios en imágenes de angiografía, un desafío debido al ruido, los artefactos y la complejidad de las estructuras vasculares. Se comparan dos enfoques de segmentación, W-Net y Segment Anything Model (SAM), y se propone un modelo híbrido que combina las capacidades especializadas de W-Net con la precisión de segmentación de SAM. La estrategia desarrollada utiliza un mapa generado por W-Net para guiar la selección de las segmentaciones producidas por SAM. Los resultados muestran que, aunque el modelo híbrido presenta una menor similitud con las pseudo-etiquetas de entrenamiento, genera segmentaciones visualmente más coherentes y anatómicamente más precisas, evidenciando su potencial para mejorar la segmentación no supervisada de imágenes de angiografía coronaria.","This paper addresses the problem of unsupervised segmentation of coronary vessels in angiography images, a challenge posed by noise, artifacts, and the complexity of vascular structures. Two segmentation approaches, W-Net and the Segment Anything Model (SAM), are compared, and a hybrid model is proposed that combines the specialized capabilities of W-Net with the segmentation accuracy of SAM. The developed strategy uses a map generated by W-Net to guide the selection of segmentations produced by SAM. The results show that, although the hybrid model exhibits less similarity to the training pseudo-labels, it generates visually more coherent and anatomically more accurate segmentations, demonstrating its potential to improve unsupervised segmentation of coronary angiography images."],"dc:format":["70 pp","application/pdf"],"dc:identifier":["https://repository.urosario.edu.co/handle/10336/48082"],"dc:language":["spa"],"dc:publisher":["Universidad del Rosario","Escuela Colombiana de Ingeniería Julio Garavito","Escuela de Medicina y Ciencias de la Salud","Maestría en Ingeniería Biomédica"],"dc:rights":["info:eu-repo/semantics/openAccess","http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:source":["J. El-Taraboulsi, C. P. Cabrera, C. Roney, and N. Aung, “Deep neural network architectures for cardiac image segmentation,” Artificial Intelligence in the Life Sciences, vol. 4, p. 100083, Dec. 2023, doi: 10.1016/j.ailsci.2023.100083.","C. Bian et al., “Uncertainty-aware domain alignment for anatomical structure segmentation,” Med. Image Anal., vol. 64, p. 101732, Aug. 2020, doi: 10.1016/j.media.2020.101732","X. Xia and B. Kulis, “W-Net: A Deep Model for Fully Unsupervised Image Segmentation,” Nov. 2017","A. Medellin, D. Grabowsky, D. Mikulski, and R. Langari, “Sam-Sam - A Novel Approach to Hyperspectral Image Semantic Segmentation,” in 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, Oct. 2023, pp. 1–5. doi: 10.1109/WHISPERS61460.2023.10431253.","W. Chen et al., “Prostate Segmentation using 2D Bridged U-net,” in 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2019, pp. 1–7. doi: 10.1109/IJCNN.2019.8851908.","Y. Zhang, Z. Shen, and R. Jiao, “Segment anything model for medical image segmentation: Current applications and future directions,” Comput. Biol. Med., vol. 171, p. 108238, Mar. 2024, doi: 10.1016/j.compbiomed.2024.108238.","T.-Y. Hsia, E. A. Peck, and J. V. Conte, “Coronary Artery Disease,” in The Johns Hopkins Manual of Cardiac Surgical Care, Elsevier, 2008, pp. 61–93. doi: 10.1016/B978-0-323-01810-4.10003-3.","B. A. Stark et al., “Global, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990-2023,” JACC, Sep. 2025, doi: 10.1016/j.jacc.2025.08.015.","S. Lee, M. Lee, and M. Kang, “Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain,” Sensors, vol. 18, no. 4, p. 1019, Mar. 2018, doi: 10.3390/s18041019.","LUQMAN and FAKHRUD DIN, “A Hyper-Heuristic Based Strategy for Image Segmentation Using Multilevel Thresholding,” Journal of Quality Measurement and Analysis, vol. 21, no. 2, pp. 123–152, Jun. 2025, doi: 10.17576/jqma.2102.2025.10.","Y. 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