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Found 126 papers, 55 papers with code
SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field.
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference
To ensure a global understanding of the surgical procedure, we devise a phase localization strategy for SurgPLAN++ to predict phase segments across the entire video through phase proposals.
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels
In this work, we present EndoOmni, the first foundation model for zero-shot cross-domain depth estimation for endoscopy.
SurgeMOD: Translating image-space tissue motions into vision-based surgical forces
Using internal movements generated by natural processes like breathing or the cardiac cycle, we infer the image-space basis of the motion on the frequency domain.
Self-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images
In this manuscript, we propose a comprehensive framework to efficiently generate new samples for training a brain lesion segmentation model.
VS-Assistant: Versatile Surgery Assistant on the Demand of Surgeons
In this work, by leveraging advanced multimodal large language models (MLLMs), we propose a Versatile Surgery Assistant (VS-Assistant) that can accurately understand the surgeon's intention and complete a series of surgical understanding tasks, e. g., surgical scene analysis, surgical instrument detection, and segmentation on demand.
Artificial Intelligence in the Autonomous Navigation of Endovascular Interventions: A Systematic Review
Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions.
A self-supervised text-vision framework for automated brain abnormality detection
To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports.
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
To address this, we developed a recurrent transformer model that tracks a surgeon's performance throughout a session by mapping hidden states to six OSATS, derived from kinematic data, using a clinically motivated objective function.
Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation
A deep learning model is trained to predict merged labels.
Framework to generate perfusion map from CT and CTA images in patients with acute ischemic stroke: A longitudinal and cross-sectional study
Stroke is a leading cause of disability and death.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
MatchSeg: Towards Better Segmentation via Reference Image Matching
Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs.
DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography
Accurate identification of End-Diastolic (ED) and End-Systolic (ES) frames is key for cardiac function assessment through echocardiography.
Rethinking Low-quality Optical Flow in Unsupervised Surgical Instrument Segmentation
Video-based surgical instrument segmentation plays an important role in robot-assisted surgeries.
SuPRA: Surgical Phase Recognition and Anticipation for Intra-Operative Planning
In conclusion, SuPRA presents a new multi-task approach that paves the way for improved intra-operative assistance through surgical phase recognition and prediction of future events.
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy
Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed.
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks
First, we introduce an innovative adaptive noise scale that effectively generates the textual elements with more variability while preserving the original text feature's integrity.
DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery
In this paper, we present a new vision-haptic dataset (DaFoEs) with variable soft environments for the training of deep neural models.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning.
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities.
An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field
In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning
In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions.
Unsupervised 3D out-of-distribution detection with latent diffusion models
We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs).
ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance
In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic.
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
LoViT: Long Video Transformer for Surgical Phase Recognition
Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos.
Ranked #1 on
Surgical phase recognition
on Cholec80
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach with limited training data.
Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks.
VideoSum: A Python Library for Surgical Video Summarization
It is thus unsurprising that substantial research efforts are made to develop methods aiming at mitigating the scarcity of annotated SDS data.
Individualized prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations.
SKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition
We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
Denoising diffusion models for out-of-distribution detection
We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs.
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction
We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR.
Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines.
Brain Imaging Generation with Latent Diffusion Models
In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.
Morphology-preserving Autoregressive 3D Generative Modelling of the Brain
Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder
Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network.
Driving Points Prediction For Abdominal Probabilistic Registration
Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images.
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid.
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling.
Transformer-based out-of-distribution detection for clinically safe segmentation
We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
Augmentation based unsupervised domain adaptation
The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellum of the Tc1 mouse model of Down Syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI
Down Syndrome is a chromosomal disorder that affects the development of cerebellar cortical lobules.
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.
Acquisition-invariant brain MRI segmentation with informative uncertainties
Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses.
The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases.
Deep forecasting of translational impact in medical research
We show that citations are only moderately predictive of translational impact as judged by inclusion in patents, guidelines, or policy documents.
A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation
We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality.
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation.
Neuradicon: operational representation learning of neuroimaging reports
Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis.
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects of interest.
Estimating MRI Image Quality via Image Reconstruction Uncertainty
Thus, we argue that quality control for visual assessment cannot be equated to quality control for algorithmic processing.
Automated triaging of head MRI examinations using convolutional neural networks
The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects.
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.
Unsupervised Brain Anomaly Detection and Segmentation with Transformers
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic.
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
A future work path of high clinical relevance is the extension of the classification to ESCN types.
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Background data is captured by collecting videos that do not contain instruments.
Scale factor point spread function matching: Beyond aliasing in image resampling
Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design.
Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications
In addition to the efficient construction of a mosaic, our framework provides, as a by-product, ground truth landmark correspondences which can be used for evaluation or learning purposes.
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger.
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.
Hierarchical brain parcellation with uncertainty
We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions.
Enhancing Fiber Orientation Distributions using convolutional Neural Networks
We evaluate how well each CNN model can resolve local fiber orientation 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models.
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos.
Scribble-based Domain Adaptation via Co-segmentation
Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation.
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions.
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
The Future of Digital Health with Federated Learning
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE
The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner.
On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability of deep neural networks training.
SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning
Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning.
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
The performance of multi-task learning in Convolutional Neural Networks (CNNs) hinges on the design of feature sharing between tasks within the architecture.
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69. 68HU) compared to a baseline CNN (66. 25HU), but lead to significant improvement in the PET reconstruction - 115a. u.
Knowledge distillation for semi-supervised domain adaptation
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging
Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions.
Deep Sequential Mosaicking of Fetoscopic Videos
Mosaicking can align multiple overlapping images to generate an image with increased FoV, however, existing techniques apply poorly to fetoscopy due to the low visual quality, texture paucity, and hence fail in longer sequences due to the drift accumulated over time.
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Developing a DNN for such joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on a task-specific hetero-modal imaging protocol.
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Medical image processing tasks such as segmentation often require capturing non-local information.
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow.
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Large-scale mammography CAD with Deformable Conv-Nets
State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules.
Grey matter sublayer thickness estimation in themouse cerebellum
In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors.
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification.
Robust training of recurrent neural networks to handle missing data for disease progression modeling
This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks.
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks.
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources.
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery.
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.
A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography
We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations.
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data.
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.
Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras
We propose a method for geometric calibration of multi-focus plenoptic cameras using raw images.
Refractive Structure-From-Motion Through a Flat Refractive Interface
By explicitly considering a refractive interface, we develop a succinct derivation of the refractive fundamental matrix in the form of the generalised epipolar constraint for an axial camera.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation.
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2014
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.
Part-to-whole Registration of Histology and MRI using Shape Elements
We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks
3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice scores achieves segmentations that are more semantically meaningful for brain tumour segmentation.