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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.
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Identification

Personal identification

Full name
Bruno André Pires da Silva

Citation names

  • Silva, Bruno

Author identifiers

Ciência ID
851E-29A3-3588
ORCID iD
0000-0002-0314-9703

Knowledge fields

  • Engineering and Technology - Electrotechnical Engineering, Electronics and Informatics
  • Engineering and Technology - Medical Engineering
  • Medical and Health Sciences - Clinical Medicine - Surgery

Languages

Language Speaking Reading Writing Listening Peer-review
Portuguese (Mother tongue)
English Upper intermediate (B2) Advanced (C1) Advanced (C1) Advanced (C1)
Education
Degree Classification
2021/09/16 - 2025/09/16
Ongoing
 Health Sciences (Doutoramento)
Major in Ciências da Saúde
Universidade do Minho Escola de Ciências da Saúde, Portugal
"Semantic Segmentation of Tissues and Anatomical Structures of 3D Laparoscopic Videos Using Deep Learning" (THESIS/DISSERTATION)
2015/09/07 - 2021/03/07
Concluded
 Engenharia Eletrónica Industrial e Computadores (Mestrado integrado)
Major in Eletrotecnia e Sistemas de Energia, Sistemas Embebidos e Computadores, Automação Controlo e Robótica
Universidade do Minho Escola de Engenharia, Portugal
"Automatic quantitative assessment of Pectus Excavatum severity using CT images and deep learning" (THESIS/DISSERTATION)
 14
Affiliation

Other Careers

Category
Host institution 		Employer
2019/07/19 - 2019/09/20 Estagiário com Licenciatura (Especialista de informática) Synopsys Inc, United States

Others

Category
Host institution 		Employer
2021/09/16 - Current PhD Student / Researcher Karl Storz GmbH & Co KG, Germany
Universidade do Minho Instituto de Investigação em Ciências da Vida e Saúde, Portugal
Outputs

Publications

Conference paper
  1. Fernández-Rodríguez, Marcos; Silva, Bruno; Queirós, Sandro; Torres, Helena R.; Oliveira, Bruno; Morais, Pedro; Buschle, L.R; et al. "Exploring optical flow inclusion into nnU-Net framework for surgical instrument segmentation". Paper presented in SPIE 2024 Medical Imaging, San Diego, 2024.
    Accepted
  2. Silva, Bruno; Queirós, Sandro; Fernández-Rodríguez, Marcos; Oliveira, Bruno; Torres, Helena R.; Morais, Pedro; Buschle, L.R; et al. "Evaluating unsupervised optical flow for keypoint tracking in laparoscopic videos". Paper presented in SPIE Medical Imaging, San Diego, 2024.
    Accepted
  3. Silva, Bruno; Oliveira, Bruno; Morais, Pedro; Buschle, L.R; Correia-Pinto, Jorge; Lima, Estevao; Vilaca, Joao L. "Analysis of Current Deep Learning Networks for Semantic Segmentation of Anatomical Structures in Laparoscopic Surgery". Paper presented in IEEE Engineering in Medicine and Biology Society, 2022.
    Published • 10.1109/EMBC48229.2022.9871583
Journal article
  1. Joao Cartucho; Alistair Weld; Samyakh Tukra; Haozheng Xu; Hiroki Matsuzaki; Taiyo Ishikawa; Minjun Kwon; et al. "SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery". Medical Image Analysis (2024): http://dx.doi.org/10.1016/j.media.2023.102985.
    10.1016/j.media.2023.102985
  2. Silva, Bruno; Pessanha, Ines; Correia-Pinto, Jorge; Fonseca, Jaime C.; Queirós, Sandro. "Automatic Assessment of Pectus Excavatum Severity From CT Images Using Deep Learning". IEEE Journal of Biomedical and Health Informatics 26 1 (2022): 324-333. http://dx.doi.org/10.1109/jbhi.2021.3090966.
    Published • 10.1109/jbhi.2021.3090966
Distinctions

Award

2024 1st Place – STIR Sub-Challenge (2D Tracking Latency), Endoscopic Vision Challenge, MICCAI 2024
Medical Image Computing and Computer Assisted Intervention Society, United States
2024 1st Place – STIR Sub-Challenge (3D Tracking Accuracy), Endoscopic Vision Challenge, MICCAI 2024
Medical Image Computing and Computer Assisted Intervention Society, United States