Publications (20)
Image To Tree with Recursive Prompting
James Batten, Matthew Sinclair, Ben Glocker +1
Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typi…
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks
Matthew Sinclair, Andreas Schuh, Karl Hahn +5
Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and do not guarantee a…
Improved 3D Whole Heart Geometry from Sparse CMR Slices
Yiyang Xu, Hao Xu, Matthew Sinclair +6
Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically…
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante +8
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the v…
Weakly Supervised Localisation for Fetal Ultrasound Images
Nicolas Toussaint, Bishesh Khanal, Matthew Sinclair +4
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any…
Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors…
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Samuel Budd, Matthew Sinclair, Bishesh Khanal +6
Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are s…
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
Yuanwei Li, Amir Alansary, Juan J. Cerrolaza +7
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn t…
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins
Matthew Sinclair, Moeen Meigooni, Archit Vasan +14
Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain lon…
Vector Representations of Vessel Trees
James Batten, Michiel Schaap, Matthew Sinclair +2
We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks. Our approach employs two sequentially trained…
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation
Jingjie Guo, Weitong Zhang, Matthew Sinclair +2
Convolutional neural networks (CNNs) often suffer from poor performance when tested on target data that differs from the training (source) data distribution, particularly in medica…
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Qingjie Meng, Matthew Sinclair, Veronika Zimmer +11
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a stand…
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Yuanwei Li, Bishesh Khanal, Benjamin Hou +8
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring…
Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation is a powerful tool for augmenting training data, de-biasing datasets, and modeling disease. Current approaches rely on external classifiers or regre…
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Liu Li, Qiang Ma, Matthew Sinclair +6
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in…
Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control
Esther Puyol Anton, Bram Ruijsink, Christian F. Baumgartner +4
Tissue characterisation with CMR parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadol…
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Matthew Sinclair, Christian F. Baumgartner, Jacqueline Matthew +9
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevan…
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Wenjia Bai, Matthew Sinclair, Giacomo Tarroni +20
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accura…