Zobrazeno 1 - 10
of 742
pro vyhledávání: '"Clarkson, P. J"'
Autor:
Das, Adrito, Sidiqi, Bilal, Mennillo, Laurent, Mao, Zhehua, Brudfors, Mikael, Xochicale, Miguel, Khan, Danyal Z., Newall, Nicola, Hanrahan, John G., Clarkson, Matthew J., Stoyanov, Danail, Marcus, Hani J., Bano, Sophia
Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective; labour-intensive; and requires domain specific expertise. Automated data driven metrics can alleviate these difficulties, as demonstrat
Externí odkaz:
http://arxiv.org/abs/2409.17025
Autor:
Zeinoddin, Mona Sheikh, Lena, Chiara, Qu, Jiongqi, Carlini, Luca, Magro, Mattia, Kim, Seunghoi, De Momi, Elena, Bano, Sophia, Grech-Sollars, Matthew, Mazomenos, Evangelos, Alexander, Daniel C., Stoyanov, Danail, Clarkson, Matthew J., Islam, Mobarakol
Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, directly applying them to surgery often yields suboptimal results. Full
Externí odkaz:
http://arxiv.org/abs/2408.17433
Autor:
Birlo, Manuel, Caramalau, Razvan, Edwards, Philip J. "Eddie", Dromey, Brian, Clarkson, Matthew J., Stoyanov, Danail
We present HUP-3D, a 3D multi-view multi-modal synthetic dataset for hand-ultrasound (US) probe pose estimation in the context of obstetric ultrasound. Egocentric markerless 3D joint pose estimation has potential applications in mixed reality based m
Externí odkaz:
http://arxiv.org/abs/2407.09215
Autor:
Li, Qi, Shen, Ziyi, Yang, Qianye, Barratt, Dean C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transformations is often accepted as the learning objective, especially when t
Externí odkaz:
http://arxiv.org/abs/2407.05767
Autor:
Saeed, Shaheer U., Huang, Shiqi, Ramalhinho, João, Gayo, Iani J. M. B., Montaña-Brown, Nina, Bonmati, Ester, Pereira, Stephen P., Davidson, Brian, Barratt, Dean C., Clarkson, Matthew J., Hu, Yipeng
Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSS methods have attract
Externí odkaz:
http://arxiv.org/abs/2405.16628
Autor:
He, Runlong, Xu, Mengya, Das, Adrito, Khan, Danyal Z., Bano, Sophia, Marcus, Hani J., Stoyanov, Danail, Clarkson, Matthew J., Islam, Mobarakol
Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and facilitate intuitive surgeon-AI interaction. However, the development of
Externí odkaz:
http://arxiv.org/abs/2405.13949
Purpose: The recent Segment Anything Model (SAM) has demonstrated impressive performance with point, text or bounding box prompts, in various applications. However, in safety-critical surgical tasks, prompting is not possible due to (i) the lack of p
Externí odkaz:
http://arxiv.org/abs/2404.14040
Autor:
Pocius, Martynas, Yan, Wen, Barratt, Dean C., Emberton, Mark, Clarkson, Matthew J., Hu, Yipeng, Saeed, Shaheer U.
In this paper we propose a reinforcement learning based weakly supervised system for localisation. We train a controller function to localise regions of interest within an image by introducing a novel reward definition that utilises non-binarised cla
Externí odkaz:
http://arxiv.org/abs/2402.13778
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani J. M. B., Yang, Qianye, Min, Zhe, Saeed, Shaheer U., Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Barratt, Dean C., Prisacariu, Victor A., Hu, Yipeng
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupe
Externí odkaz:
http://arxiv.org/abs/2402.10728
Autor:
Ali, Sharib, Espinel, Yamid, Jin, Yueming, Liu, Peng, Güttner, Bianca, Zhang, Xukun, Zhang, Lihua, Dowrick, Tom, Clarkson, Matthew J., Xiao, Shiting, Wu, Yifan, Yang, Yijun, Zhu, Lei, Sun, Dai, Li, Lan, Pfeiffer, Micha, Farid, Shahid, Maier-Hein, Lena, Buc, Emmanuel, Bartoli, Adrien
Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image. Preoperative 3D models extracted from CT or
Externí odkaz:
http://arxiv.org/abs/2401.15753