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pro vyhledávání: '"Ourselin, Sébastien"'
Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field. However, current approaches based on Convolutional Neural Networks (CNNs) and Transformers often fail to capture fine-grain
Externí odkaz:
http://arxiv.org/abs/2411.12755
Autor:
Chen, Zhen, Luo, Xingjian, Wu, Jinlin, Bai, Long, Lei, Zhen, Ren, Hongliang, Ourselin, Sebastien, Liu, Hongbin
Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, t
Externí odkaz:
http://arxiv.org/abs/2409.12467
Autor:
Tian, Qingyao, Chen, Zhen, Liao, Huai, Huang, Xinyan, Li, Lujie, Ourselin, Sebastien, Liu, Hongbin
Single-image depth estimation is essential for endoscopy tasks such as localization, reconstruction, and augmented reality. Most existing methods in surgical scenes focus on in-domain depth estimation, limiting their real-world applicability. This co
Externí odkaz:
http://arxiv.org/abs/2409.05442
Autor:
Reyzabal, Mikel De Iturrate, Malas, Dionysios, Wang, Shuai, Ourselin, Sebastien, Liu, Hongbin
We present a new approach for vision-based force estimation in Minimally Invasive Robotic Surgery based on frequency domain basis of motion of organs derived directly from video. Using internal movements generated by natural processes like breathing
Externí odkaz:
http://arxiv.org/abs/2406.17707
Accurate brain lesion delineation is important for planning neurosurgical treatment. Automatic brain lesion segmentation methods based on convolutional neural networks have demonstrated remarkable performance. However, neural network performance is c
Externí odkaz:
http://arxiv.org/abs/2406.14826
Autor:
Chen, Zhen, Luo, Xingjian, Wu, Jinlin, Chan, Danny T. M., Lei, Zhen, Wang, Jinqiao, Ourselin, Sebastien, Liu, Hongbin
The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single
Externí odkaz:
http://arxiv.org/abs/2405.08272
Autor:
Robertshaw, Harry, Karstensen, Lennart, Jackson, Benjamin, Sadati, Hadi, Rhode, Kawal, Ourselin, Sebastien, Granados, Alejandro, Booth, Thomas C
Publikováno v:
(2023) Front. Hum. Neurosci. 17:1239374
Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. This systematic review explores
Externí odkaz:
http://arxiv.org/abs/2405.03305
Autor:
Wood, David A., Guilhem, Emily, Kafiabadi, Sina, Busaidi, Ayisha Al, Dissanayake, Kishan, Hammam, Ahmed, Mansoor, Nina, Townend, Matthew, Agarwal, Siddharth, Wei, Yiran, Mazumder, Asif, Barker, Gareth J., Sasieni, Peter, Ourselin, Sebastien, Cole, James H., Booth, Thomas C.
Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification
Externí odkaz:
http://arxiv.org/abs/2405.02782
In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons during training. These metrics, along with performance feedback, help surg
Externí odkaz:
http://arxiv.org/abs/2407.05180
Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduce
Externí odkaz:
http://arxiv.org/abs/2404.10572