A5028279799- Medical Image Segmentation Techniques
- Photoacoustic and Ultrasonic Imaging
- Fetal and Pediatric Neurological Disorders
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neural Network Applications
- Optical Imaging and Spectroscopy Techniques
- Domain Adaptation and Few-Shot Learning
- AI in cancer detection
- Medical Imaging and Analysis
- Robotics and Sensor-Based Localization
- Medical Imaging Techniques and Applications
- Colorectal Cancer Screening and Detection
- Meningioma and schwannoma management
- Advanced Vision and Imaging
- Surgical Simulation and Training
- Ultrasound Imaging and Elastography
- Nanoplatforms for cancer theranostics
- Advanced Neuroimaging Techniques and Applications
- Neonatal and fetal brain pathology
- Pregnancy and preeclampsia studies
- Prenatal Screening and Diagnostics
- Optical Coherence Tomography Applications
- Advanced MRI Techniques and Applications
- Anatomy and Medical Technology
- Assisted Reproductive Technology and Twin Pregnancy
King's College London
2018-2025
KU Leuven
2017-2024
King's College School
2022-2024
University of Bristol
2024
University College London
2015-2023
Wellcome / EPSRC Centre for Interventional and Surgical Sciences
2017-2023
St Thomas' Hospital
2018-2023
Lambeth Hospital
2020-2023
Kings Health Partners
2018-2023
Universidade do Porto
2023
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they not demonstrated sufficiently accurate and robust results clinical use. In addition, are limited by the lack of image-specific adaptation generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework incorporating CNNs into bounding box...
Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms flexible but do not provide specific functionality for medical adapting them this domain of application requires substantial implementation effort. Consequently, there has been duplication effort incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform deep...
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties CNN-based 2D 3D tasks. We additionally propose a test-time augmentation-based aleatoric to effect transformations input on output. Test-time augmentation has been...
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance automatic segmentation. However, they are still challenged by complicated conditions where the target has large variations position, shape scale, existing CNNs a poor explainability that limits their application to clinical decisions. In this work, we make extensive use multiple attentions in CNN architecture...
EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform fair and meaningful comparison registration algorithms which are applied to database intrapatient thoracic CT image pairs. Evaluation nonrigid techniques nontrivial task. This compounded by the fact that researchers typically test only on their own data, varies widely. For this reason, reliable assessment different has been virtually impossible in past. In work we present results launch phase...
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic methods. However, fully results may still need to be refined accurate robust enough clinical use. We propose a deep learning-based interactive method improve obtained by an CNN reduce user interactions during refinement higher accuracy. use one obtain initial segmentation, on which are added indicate...
One of the fundamental challenges in supervised learning for multimodal image registration is lack ground-truth voxel-level spatial correspondence. This work describes a method to infer transformation from higher-level correspondence information contained anatomical labels. We argue that such labels are more reliable and practical obtain reference sets pairs than Typical interest may include solid organs, vessels, ducts, structure boundaries other subject-specific ad hoc landmarks. The...
We present the Spherical Demons algorithm for registering two spherical images. By exploiting vector spline interpolation theory, we show that a large class of regularizors modified objective function can be efficiently approximated on sphere using iterative smoothing. Based one parameter subgroups diffeomorphisms, resulting registration is diffeomorphic and fast. The also to register given image probabilistic atlas. demonstrate variants corresponding warping atlas or subject. Registration...
Section:ChooseTop of pageAbstract <<METHODSRESULTSORIGINS OF THE BRONCHIAL ...FCFM ENDOSCOPY PATHOLO...DISCUSSIONReferencesCITING ARTICLES
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications AI in healthcare have the potential to improve our ability detect, diagnose, prognose, and intervene on human disease. For models be used clinically, they need made safe, reproducible robust, underlying software framework must aware particularities (e.g. geometry, physiology, physics) medical data being processed. This work introduces MONAI, freely available, community-supported,...
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing methods are time-consuming and often require user interactions to localize extract the several slices. We propose a fully automatic framework that consists four stages: 1) localization based on coarse segmentation by Convolutional Neural Network (CNN), 2) fine another CNN trained with multi-scale loss...
Automatic segmentation of brain tumors from medical images is important for clinical assessment and treatment planning tumors. Recent years have seen an increasing use convolutional neural networks (CNNs) this task, but most them either 2D with relatively low memory requirement while ignoring 3D context, or exploiting features large consumption. In addition, existing methods rarely provide uncertainty information associated the result. We propose a cascade CNNs to segment hierarchical...
Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and critical importance in data science. We propose two novel deep learning architectures for automatic non-rigid instruments. Both methods take advantage automated deep-learning-based multi-scale feature extraction while trying to maintain accurate quality at all resolutions. The proposed encode the constraint inside network architecture. first architecture enforces it...