Bruno Silva graduated in 2021 with an M.Sc. degree in Industrial Electronics and Computers Engineering by the University of Minho (UM, Portugal). His master’s thesis was related with the automatic quantification of Pectus Excavatum depression using medical images and artificial intelligence (AI), which led to the publication of 1 article in a peer-reviewed journal (IEEE Journal of Biomedical and Health Informatics). In 2021, he joined the PhD programme in Health Sciences at the University of Minho (Portugal). Since 2019, Bruno Silva is a researcher at the Life and Health Sciences Research Institute (ICVS), School of Medicine, UM. His work is mainly focused on the development of computer-assisted systems using medical imaging and AI to support clinicians. Current work involves creating software for tracking and segmenting anatomical landmarks in laparoscopic surgeries, providing surgeons with valuable feedback to enhance their capabilities and efficiency.
Bruno Silva graduated in 2021 with an M.Sc. degree in Industrial Electronics and Computers Engineering by the University of Minho (UM, Portugal). His master’s thesis was related with the automatic quantification of Pectus Excavatum depression using medical images and artificial intelligence (AI), which led to the publication of 1 article in a peer-reviewed journal (IEEE Journal of Biomedical and Health Informatics). In 2021, he joined the PhD programme in Health Sciences at the University of Minho (Portugal). Since 2019, Bruno Silva is a researcher at the Life and Health Sciences Research Institute (ICVS), School of Medicine, UM. His work is mainly focused on the development of computer-assisted systems using medical imaging and AI to support clinicians. Current work involves creating software for tracking and segmenting anatomical landmarks in laparoscopic surgeries, providing surgeons with valuable feedback to enhance their capabilities and efficiency.
– B. Silva, I. Pessanha, J. Correia-Pinto, J. C. Fonseca, S. Queirós, “Automatic assessment of Pectus Excavatum severity from CT images using deep learning”, IEEE Journal of Biomedical Health Informatics, vol. 26, pp. 324-333, 2022. DOI: 10.1109/JBHI.2021.3090966
– B. Silva, B. Oliveira, P. Morais, L. R. Buschle, J. Correia-Pinto, E. Lima, J. L. Vilaça, “Analysis of Current Deep Learning Networks for Semantic Segmentation of Anatomical Structures in Laparoscopic Surgery”, 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022.
– J. Cartucho, …, B. Silva, E. Lima, J. L. Vilaça, S. Queirós, S. Giannarou, “SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery”, Medical Image Analysis, vol. 91, pp. 102985, 2024. DOI: 10.1016/j.media.2023.102985
This project aims to develop endoscopic, robotic and artificially intelligent surgical instruments that facilitate the performance of minimally invasive surgery. We are creating new surgical navigation systems using artificial intelligence…
Phone: +351 253 604 967
Fax: +351 253 604 809
Email: icvs.sec@med.uminho.pt
Life and Health Sciences
Research Institute (ICVS)
School of Medicine,
University of Minho,
Campus de Gualtar
4710-057 Braga
Portugal
Copyright ©2022 ICVS. All Rights Reserved
Copyright ©2022 ICVS. All Rights Reserved
Life and Health Sciences
Research Institute (ICVS)
School of Medicine,
University of Minho,
Campus de Gualtar
4710-057 Braga
Portugal
Copyright ©2022 ICVS. All Rights Reserved
Life and Health Sciences
Research Institute (ICVS)
School of Medicine,
University of Minho,
Campus de Gualtar
4710-057 Braga
Portugal
