Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Herrera, Luis C."'
Autor:
Wei, Meng, Budd, Charlie, Garcia-Peraza-Herrera, Luis C., Dorent, Reuben, Shi, Miaojing, Vercauteren, Tom
Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions. In this work, we propose SegMatch, a semi supervised learning method to reduce the need for expensive
Externí odkaz:
http://arxiv.org/abs/2308.05232
Autor:
Liu, Yang, Boels, Maxence, Garcia-Peraza-Herrera, Luis C., Vercauteren, Tom, Dasgupta, Prokar, Granados, Alejandro, Ourselin, Sebastien
Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they train spatial feature extractors u
Externí odkaz:
http://arxiv.org/abs/2305.08989
Autor:
Garcia-Peraza-Herrera, Luis C., Horgan, Conor, Ourselin, Sebastien, Ebner, Michael, Vercauteren, Tom
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far
Externí odkaz:
http://arxiv.org/abs/2303.08252
The performance of deep learning (DL) algorithms is heavily influenced by the quantity and the quality of the annotated data. However, in Surgical Data Science, access to it is limited. It is thus unsurprising that substantial research efforts are ma
Externí odkaz:
http://arxiv.org/abs/2303.10173
Autor:
Budd, Charlie, Garcia-Peraza-Herrera, Luis C., Huber, Martin, Ourselin, Sebastien, Vercauteren, Tom
Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pi
Externí odkaz:
http://arxiv.org/abs/2210.14771
The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in the system and changes the tra
Externí odkaz:
http://arxiv.org/abs/2210.11988
Autor:
Gruijthuijsen, Caspar, Garcia-Peraza-Herrera, Luis C., Borghesan, Gianni, Reynaerts, Dominiek, Deprest, Jan, Ourselin, Sebastien, Vercauteren, Tom, Poorten, Emmanuel Vander
Many keyhole interventions rely on bi-manual handling of surgical instruments, forcing the main surgeon to rely on a second surgeon to act as a camera assistant. In addition to the burden of excessively involving surgical staff, this may lead to redu
Externí odkaz:
http://arxiv.org/abs/2107.02317
Autor:
Garcia-Peraza-Herrera, Luis C., Everson, Martin, Lovat, Laurence, Wang, Hsiu-Po, Wang, Wen Lun, Haidry, Rehan, Stoyanov, Danail, Ourselin, Sebastien, Vercauteren, Tom
Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection (CADe) system that can classify st
Externí odkaz:
http://arxiv.org/abs/2102.09963
Autor:
Garcia-Peraza-Herrera, Luis C., Fidon, Lucas, D'Ettorre, Claudia, Stoyanov, Danail, Vercauteren, Tom, Ourselin, Sebastien
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks for instrument-backgro
Externí odkaz:
http://arxiv.org/abs/2102.09528
Autor:
García-Peraza-Herrera, Luis C., Li, Wenqi, Gruijthuijsen, Caspar, Devreker, Alain, Attilakos, George, Deprest, Jan, Poorten, Emmanuel Vander, Stoyanov, Danail, Vercauteren, Tom, Ourselin, Sébastien
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. Our method exploits the ability of deep
Externí odkaz:
http://arxiv.org/abs/2009.03016