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Found 61 papers, 20 papers with code
Trustworthy Contrast-enhanced Brain MRI Synthesis
Contrast-enhanced brain MRI (CE-MRI) is a valuable diagnostic technique but may pose health risks and incur high costs.
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset.
Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
With the proposed methods, we can enhance concept explanations' reliability for both supervised and label-efficient settings.
Toward Universal Medical Image Registration via Sharpness-Aware Meta-Continual Learning
Current deep learning approaches in medical image registration usually face the challenges of distribution shift and data collection, hindering real-world deployment.
MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging
MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
This paper introduces Zoo-MSFDA, a more general setting that allows each source domain to offer a zoo of multiple source models with different architectures.
Bayesian Intrinsic Groupwise Image Registration: Unsupervised Disentanglement of Anatomy and Geometry
This article presents a general Bayesian learning framework for multi-modal groupwise registration on medical images.
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively.
Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation
For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively.
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
In this work, we propose an unsupervised framework for multi-class segmentation with both intensity and shape constraints.
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.
ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation
Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images.
MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images
We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences.
$\mathcal{X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images.
Multi-Modality Cardiac Image Computing: A Survey
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases.
Deep Compatible Learning for Partially-Supervised Medical Image Segmentation
To address the challenge, we propose a deep compatible learning (DCL) framework, which trains a single multi-label segmentation network using images with only partial structures annotated.
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data.
Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation
To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation.
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement
Multimodal groupwise registration aligns internal structures in a group of medical images.
ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
To tackle this problem, we propose a new scribble-guided method for cardiac segmentation, based on the Positive-Unlabeled (PU) learning framework and global consistency regularization, and termed as ShapePU.
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors.
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
To address the difficulties, we propose a new framework for scribble learning-based medical image segmentation, which is composed of mix augmentation and cycle consistency and thus is referred to as CycleMix.
Ranked #3 on
Semantic Segmentation
on ACDC Scribbles
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
Multi-Modality Cardiac Image Analysis with Deep Learning
Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease.
Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition
In this work, we propose an automatic RV segmentation framework, where the information from long-axis (LA) views is utilized to assist the segmentation of short-axis (SA) views via information transition.
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation.
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Unsupervised domain adaptation is useful in medical image segmentation.
A low-rank representation for unsupervised registration of medical images
These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training.
Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information
However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training.
VSpSR: Explorable Super-Resolution via Variational Sparse Representation
Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image.
Learning-Based Algorithms for Vessel Tracking: A Review
On the basis of the reviewed methods, the evaluation issues are introduced.
Rank-One Network: An Effective Framework for Image Restoration
The RO decomposition is developed to decompose a corrupted image into the RO components and residual.
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks
We embed the orientation recognition network in a Cardiac MRI Orientation Adjust Tool, i. e., CMRadjustNet.
Anatomy Prior Based U-net for Pathology Segmentation with Attention
Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases.
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint
Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches.
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style.
MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training.
Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention
Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
KLDivNet: An unsupervised neural network for multi-modality image registration
We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images.
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN).
Multi-scale deep neural networks for real image super-resolution
Single image super-resolution (SR) is extremely difficult if the upscaling factors of image pairs are unknown and different from each other, which is common in real image SR. To tackle the difficulty, we develop two multi-scale deep neural networks (MsDNN) in this work.
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD.
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).
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.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.
Atrial scars segmentation via potential learning in the graph-cuts framework
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients.
Multivariate mixture model for myocardium segmentation combining multi-source images
The segmentation is a procedure of texture classification, and the MvMM is used to model the joint intensity distribution of the images.
