no code implementations • 21 Feb 2024 • Martynas Pocius, Wen Yan, Dean C. Barratt, Mark Emberton, Matthew J. Clarkson, Yipeng Hu, Shaheer U. Saeed
The object-presence classifier may then inform the controller of its localisation quality by quantifying the likelihood of the image containing an object.
no code implementations • 16 Feb 2024 • Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo, Qianye Yang, Zhe Min, Shaheer U. Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Dean C. Barratt, Victor A. Prisacariu, Yipeng Hu
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 unsupervised losses are unavailable or ineffective.
no code implementations • 22 Aug 2023 • Weixi Yi, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Yipeng Hu, Shaheer U. Saeed
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training.
1 code implementation • 10 Mar 2023 • Yunguan Fu, Yiwen Li, Shaheer U. Saeed, Matthew J. Clarkson, Yipeng Hu
Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation.
no code implementations • 3 Mar 2023 • Shaheer U. Saeed, Tom Syer, Wen Yan, Qianye Yang, Mark Emberton, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Yipeng Hu
For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2. 9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes.
1 code implementation • 20 Feb 2023 • Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed, Mark Emberton, Dean C. Barratt, Zeike A. Taylor, Yipeng Hu
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible.
no code implementations • 3 Dec 2022 • Shaheer U. Saeed, João Ramalhinho, Mark Pinnock, Ziyi Shen, Yunguan Fu, Nina Montaña-Brown, Ester Bonmati, Dean C. Barratt, Stephen P. Pereira, Brian Davidson, Matthew J. Clarkson, Yipeng Hu
In this work, the task predictor is a segmentation network.
no code implementations • 21 Jul 2022 • Iani JMB Gayo, Shaheer U. Saeed, Dean C. Barratt, Matthew J. Clarkson, Yipeng Hu
However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequential decision making process that involves navigating an ultrasound probe and placing a series of sampling needles for potentially multiple targets.
1 code implementation • 27 Mar 2022 • Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability.
1 code implementation • 20 Feb 2022 • Shaheer U. Saeed, Wen Yan, Yunguan Fu, Francesco Giganti, Qianye Yang, Zachary M. C. Baum, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Mark Emberton, Dean C. Barratt, Yipeng Hu
This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.
1 code implementation • 31 Jul 2021 • Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.
no code implementations • 15 Feb 2021 • Shaheer U. Saeed, Yunguan Fu, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, Dean C. Barratt, Yipeng Hu
In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation.
1 code implementation • 4 Nov 2020 • Yunguan Fu, Nina Montaña Brown, Shaheer U. Saeed, Adrià Casamitjana, Zachary M. C. Baum, Rémi Delaunay, Qianye Yang, Alexander Grimwood, Zhe Min, Stefano B. Blumberg, Juan Eugenio Iglesias, Dean C. Barratt, Ester Bonmati, Daniel C. Alexander, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu
DeepReg (https://github. com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
no code implementations • 9 Jul 2020 • Shaheer U. Saeed, Zeike A. Taylor, Mark A. Pinnock, Mark Emberton, Dean C. Barratt, Yipeng Hu
Based on 160, 000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0. 017 mm in predicted nodal displacement.