Search Results for author: Yipeng Hu

Found 61 papers, 24 papers with code

One registration is worth two segmentations

1 code implementation17 May 2024 Shiqi Huang, Tingfa Xu, Ziyi Shen, Shaheer Ullah Saeed, Wen Yan, Dean Barratt, Yipeng Hu

The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e. g., rigid, affine, and splines).

Image Registration

An unsupervised learning-based shear wave tracking method for ultrasound elastography

no code implementations25 Apr 2024 Remi Delaunay, Yipeng Hu, Tom Vercauteren

Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties.

Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images

no code implementations21 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.

Multiple Instance Learning Object

Semi-weakly-supervised neural network training for medical image registration

no code implementations16 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.

Image Registration Medical Image Registration

Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker

1 code implementation16 Oct 2023 Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu

Significance: The proposed new methodology with publicly available volunteer data and code for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application.

3D Reconstruction

Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction

1 code implementation20 Aug 2023 Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu

Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs).


Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation

no code implementations17 Jul 2023 Wen Yan, Bernard Chiu, Ziyi Shen, Qianye Yang, Tom Syer, Zhe Min, Shonit Punwani, Mark Emberton, David Atkinson, Dean C. Barratt, Yipeng Hu

One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2. 1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer.

Image Segmentation Semantic Segmentation

Expectation Maximization Pseudo Labels

1 code implementation2 May 2023 MouCheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Daniel C. Alexander, Neil P. Oxtoby, Yipeng Hu, Joseph Jacob

In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.


Spatial Correspondence between Graph Neural Network-Segmented Images

no code implementations12 Mar 2023 Qian Li, Yunguan Fu, Qianye Yang, Zhijiang Du, Hongjian Yu, Yipeng Hu

Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges.

Image Segmentation Medical Image Segmentation +2

Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation

1 code implementation10 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.

Denoising Image Segmentation +2

Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion

no code implementations3 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.

Image Generation

Non-rigid Medical Image Registration using Physics-informed Neural Networks

1 code implementation20 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.

Image Registration Medical Image Registration

Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames

1 code implementation9 Nov 2022 Qi Li, Ziyi Shen, Qian Li, Dean C Barratt, Thomas Dowrick, Matthew J Clarkson, Tom Vercauteren, Yipeng Hu

Little benefit was observed by adding frames more than one second away from the predicted transformation, with or without LSTM-based RNNs.

Multi-Task Learning

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

1 code implementation12 Sep 2022 Yiwen Li, Yunguan Fu, Iani Gayo, Qianye Yang, Zhe Min, Shaheer Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.

Few-Shot Learning Segmentation

Cross-Modality Image Registration using a Training-Time Privileged Third Modality

1 code implementation26 Jul 2022 Qianye Yang, David Atkinson, Yunguan Fu, Tom Syer, Wen Yan, Shonit Punwani, Matthew J. Clarkson, Dean C. Barratt, Tom Vercauteren, Yipeng Hu

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered.

Image Registration

Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

no code implementations22 Jul 2022 Zachary MC Baum, Yipeng Hu, Dean C Barratt

Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes.

Image Registration Medical Image Registration +1

Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

no code implementations22 Jul 2022 Zachary MC Baum, Tamas Ungi, Christopher Schlenger, Yipeng Hu, Dean C Barratt

This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available.

Image Registration

Strategising template-guided needle placement for MR-targeted prostate biopsy

no code implementations21 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.

Anatomy Decision Making +1

The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation

no code implementations30 Mar 2022 Wen Yan, Qianye Yang, Tom Syer, Zhe Min, Shonit Punwani, Mark Emberton, Dean C. Barratt, Bernard Chiu, Yipeng Hu

However, the differences in false-positives and false-negatives, between the actual errors and the perceived counterparts if DSC is used, can be as high as 152 and 154, respectively, out of the 357 test set lesions.

Image Segmentation Medical Image Segmentation +3

VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

no code implementations12 Mar 2022 Yukun Zhou, MouCheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, Daniel C. Alexander

Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.


Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

1 code implementation20 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.

Image Quality Assessment

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

no code implementations17 Jan 2022 Yiwen Li, Yunguan Fu, Qianye Yang, Zhe Min, Wen Yan, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after.

Anatomy Image Segmentation +3

Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

1 code implementation22 Oct 2021 Yiwen Li, Gratianus Wesley Putra Data, Yunguan Fu, Yipeng Hu, Victor Adrian Prisacariu

Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalisation requirement for unseen classes.

Few-Shot Semantic Segmentation Segmentation +1

Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks

no code implementations12 Oct 2021 Ester Bonmati, Yipeng Hu, Alexander Grimwood, Gavin J. Johnson, George Goodchild, Margaret G. Keane, Kurinchi Gurusamy, Brian Davidson, Matthew J. Clarkson, Stephen P. Pereira, Dean C. Barratt

In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.

Anatomy Image Classification

Real-time multimodal image registration with partial intraoperative point-set data

no code implementations10 Sep 2021 Zachary M C Baum, Yipeng Hu, Dean C Barratt

Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods.

Image Registration

Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia

no code implementations6 Aug 2021 Harry Mason, Lorenzo Cristoni, Andrew Walden, Roberto Lazzari, Thomas Pulimood, Louis Grandjean, Claudia AM Gandini Wheeler-Kingshott, Yipeng Hu, Zachary MC Baum

Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.

Unsupervised Domain Adaptation

Adaptable image quality assessment using meta-reinforcement learning of task amenability

1 code implementation31 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.

Image Classification Image Quality Assessment +4

Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

1 code implementation28 Jul 2021 Liam F Chalcroft, Jiongqi Qu, Sophie A Martin, Iani JMB Gayo, Giulio V Minore, Imraj RD Singh, Shaheer U Saeed, Qianye Yang, Zachary MC Baum, Andre Altmann, Yipeng Hu

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.


Learning to Address Intra-segment Misclassification in Retinal Imaging

2 code implementations25 Apr 2021 Yukun Zhou, MouCheng Xu, Yipeng Hu, Hongxiang Lin, Joseph Jacob, Pearse A. Keane, Daniel C. Alexander

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.

Retinal Vessel Segmentation Segmentation

Learning image quality assessment by reinforcing task amenable data selection

no code implementations15 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.

Image Classification Image Quality Assessment

Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification

no code implementations6 Oct 2020 Alexander Grimwood, Helen McNair, Yipeng Hu, Ester Bonmati, Dean Barratt, Emma Harris

For images with unanimous consensus between observers, anatomical classification accuracy was 97. 2% and probe adjustment accuracy was 94. 9%.

Anatomy General Classification

Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination

no code implementations29 Aug 2020 Qianye Yang, Yunguan Fu, Francesco Giganti, Nooshin Ghavami, Qingchao Chen, J. Alison Noble, Tom Vercauteren, Dean Barratt, Yipeng Hu

Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.

Image Registration Morphological Analysis +1

Multimodality Biomedical Image Registration using Free Point Transformer Networks

no code implementations5 Aug 2020 Zachary M. C. Baum, Yipeng Hu, Dean C. Barratt

We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encountered in ultrasound-guided interventional procedures.

Image Registration

Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes

no code implementations9 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.

The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

no code implementations19 May 2020 Yipeng Hu, Joseph Jacob, Geoffrey JM Parker, David J. Hawkes, John R. Hurst, Danail Stoyanov

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.

COVID-19 Diagnosis Drug Discovery +1

More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

no code implementations20 Aug 2019 Yunguan Fu, Maria R. Robu, Bongjin Koo, Crispin Schneider, Stijn van Laarhoven, Danail Stoyanov, Brian Davidson, Matthew J. Clarkson, Yipeng Hu

Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications.

Image Segmentation Segmentation +1

Conditional Segmentation in Lieu of Image Registration

no code implementations30 Jun 2019 Yipeng Hu, Eli Gibson, Dean C. Barratt, Mark Emberton, J. Alison Noble, Tom Vercauteren

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned.

Image Registration Image Segmentation +2

Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration

no code implementations9 Jul 2018 Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.

Image Registration

Adversarial Deformation Regularization for Training Image Registration Neural Networks

1 code implementation27 May 2018 Yipeng Hu, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, Tom Vercauteren, J. Alison Noble, Dean C. Barratt

During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation.

Image Registration

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

no code implementations18 Dec 2017 Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren

Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.

Label-driven weakly-supervised learning for multimodal deformable image registration

1 code implementation5 Nov 2017 Yipeng Hu, Marc Modat, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.

Image Registration Weakly-supervised Learning

NiftyNet: a deep-learning platform for medical imaging

10 code implementations11 Sep 2017 Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I. Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C. Barratt, Sébastien Ourselin, M. Jorge Cardoso, Tom Vercauteren

NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.

Data Augmentation Image Generation +4

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

no code implementations5 Sep 2017 Yipeng Hu, Eli Gibson, Tom Vercauteren, Hashim U. Ahmed, Mark Emberton, Caroline M. Moore, J. Alison Noble, Dean C. Barratt

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image.


Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks

no code implementations17 Jul 2017 Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren, J. Alison Noble

Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration.

Anatomy Image Registration +1

Cannot find the paper you are looking for? You can Submit a new open access paper.