Medical Image Analysis

Papers
(The TQCC of Medical Image Analysis is 57. The table below lists those papers that are above that threshold based on CrossRef citation counts. The publications cover those that have been published in the past four years, i.e., from 2019-06-01 to 2023-06-01.)
ArticleCitations
Generative adversarial network in medical imaging: A review721
Attention gated networks: Learning to leverage salient regions in medical images683
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning557
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks491
Multi-atlas segmentation of biomedical images: A survey448
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis418
A deep learning framework for unsupervised affine and deformable image registration401
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images342
Segmentation of blood vessels from red-free and fluorescein retinal images310
DCAN: Deep contour-aware networks for object instance segmentation from histology images300
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI298
BACH: Grand challenge on breast cancer histology images260
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator253
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs243
Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance238
Glaucoma risk index:Automated glaucoma detection from color fundus images228
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology225
Segmentation of brain tissue from magnetic resonance images210
Self-supervised learning for medical image analysis using image context restoration209
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data206
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers194
Medical image classification using synergic deep learning189
Machine learning approaches in medical image analysis: From detection to diagnosis189
Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative172
High resolution cortical bone thickness measurement from clinical CT data171
Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models169
Right ventricle segmentation from cardiac MRI: A collation study166
Estimation of 3D left ventricular deformation from echocardiography152
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem150
BIRNet: Brain image registration using dual-supervised fully convolutional networks150
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge146
MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images143
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge141
Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images140
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces136
Integrating spatial configuration into heatmap regression based CNNs for landmark localization135
Iterative fully convolutional neural networks for automatic vertebra segmentation and identification133
Predicting soft tissue deformations for a maxillofacial surgery planning system: From computational strategies to a complete clinical validation132
Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images130
Benchmarking framework for myocardial tracking and deformation algorithms: An open access database129
Constrained-CNN losses for weakly supervised segmentation127
Automated diagnosis of breast ultrasonography images using deep neural networks127
Clinically evaluated procedure for the reconstruction of vocal fold vibrations from endoscopic digital high-speed videos124
Cardiac function estimation from MRI using a heart model and data assimilation: Advances and difficulties119
2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models115
Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image111
Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography111
Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors111
Deep vessel segmentation by learning graphical connectivity109
Micro-Net: A unified model for segmentation of various objects in microscopy images107
Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT105
Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator105
Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier103
A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion101
Surgical gesture classification from video and kinematic data100
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion98
Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions97
3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images96
Tomoelastography by multifrequency wave number recovery from time-harmonic propagating shear waves95
Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features95
Imaging the femoral cortex: Thickness, density and mass from clinical CT94
Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks94
Evaluating reinforcement learning agents for anatomical landmark detection94
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification93
Segmentation of carpal bones from CT images using skeletally coupled deformable models90
Statistical deformable bone models for robust 3D surface extrapolation from sparse data88
Towards inference of human brain connectivity from MR diffusion tensor data87
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan86
Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts84
Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model83
Cerebrovascular segmentation from TOF using stochastic models83
Disentangled representation learning in cardiac image analysis82
Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy82
The estimation of patient-specific cardiac diastolic functions from clinical measurements81
Weakly supervised mitosis detection in breast histopathology images using concentric loss81
Large-scale automatic reconstruction of neuronal processes from electron microscopy images79
Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT79
Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network79
Combined tract segmentation and orientation mapping for bundle-specific tractography78
Adversarial learning for mono- or multi-modal registration76
Denoising of 3D magnetic resonance images using a residual encoder–decoder Wasserstein generative adversarial network75
Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data74
Learning to detect lymphocytes in immunohistochemistry with deep learning74
An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: Application to MitralClip intervention planning74
Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images73
Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning69
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging68
Efficient and robust computation of PDF features from diffusion MR signal68
Model tags: direct three-dimensional tracking of heart wall motion from tagged magnetic resonance images68
Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images67
Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image67
TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set67
Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume65
In vivo strain and stress estimation of the heart left and right ventricles from MRI images64
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks64
From colour to tissue histology: Physics-based interpretation of images of pigmented skin lesions63
Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation63
Inferring brain variability from diffeomorphic deformations of currents: An integrative approach62
An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours62
Vessel extraction from non-fluorescein fundus images using orientation-aware detector61
Automatic atlas-based three-label cartilage segmentation from MR knee images61
Learning from unbalanced data: A cascade-based approach for detecting clustered microcalcifications60
Effects of biventricular pacing and scar size in a computational model of the failing heart with left bundle branch block60
CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation60
Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms59
Fast surface and volume estimation from non-parallel cross-sections, for freehand three-dimensional ultrasound58
Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images58
Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process58
Four challenges in medical image analysis from an industrial perspective58
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow58
Automatic detection of informative frames from wireless capsule endoscopy images58
Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net58
Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes57
Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling57
Reconstruction of coronary arteries from X-ray angiography: A review57
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects57
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