3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

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CE-Net: Context Encoder Network for 2D Medical Image Segmentation

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Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

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The Liver Tumor Segmentation Benchmark (LiTS)

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Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

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VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

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3D deeply supervised network for automated segmentation of volumetric medical images

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Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

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UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation

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CNN-based Segmentation of Medical Imaging DataPreprint

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DCAN: Deep contour-aware networks for object instance segmentation from histology images

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RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

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Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers

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Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images

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Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation

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Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

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Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

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Global guidance network for breast lesion segmentation in ultrasound images

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Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

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High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation

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Learning normalized inputs for iterative estimation in medical image segmentation

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Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

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UNETR: Transformers for 3D Medical Image Segmentation

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Deep learning ensemble 2D CNN approach towards the detection of lung cancer

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Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation

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3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation

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ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation

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Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

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Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training

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Dense Deconvolutional Network for Skin Lesion Segmentation

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Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

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Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

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AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes

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Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation

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Learning supervised scoring ensemble for emotion recognition in the wild

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Liver CT sequence segmentation based with improved U-Net and graph cut

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3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification

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Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

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Inter-Slice Context Residual Learning for 3D Medical Image Segmentation

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Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

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Deep learning for variational multimodality tumor segmentation in PET/CT

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SegTHOR: Segmentation of Thoracic Organs at Risk in CT images

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Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm

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Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis

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Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

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Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

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Effective lung nodule detection using deep CNN with dual attention mechanisms

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Towards a better understanding of annotation tools for medical imaging: a survey

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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation

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Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation

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Multi-task learning for the segmentation of organs at risk with label dependence

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Cephalometric landmark detection in dental x-ray images using convolutional neural networks

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3D Parallel Fully Convolutional Networks for Real-Time Video Wildfire Smoke Detection

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Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

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BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

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Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image Segmentation Under Limited Computational Resources

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3D fully convolutional networks for co-segmentation of tumors on PET-CT images

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Domain generalization on medical imaging classification using episodic training with task augmentation

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Feature Fusion Encoder Decoder Network for Automatic Liver Lesion Segmentation

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FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images

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Machine learning based liver disease diagnosis: A systematic review

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Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning

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Simultaneous cosegmentation of tumors in PET ‐ CT images using deep fully convolutional networks

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Deep Sequential Segmentation of Organs in Volumetric Medical Scans

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Liver tumor segmentation based on 3D convolutional neural network with dual scale

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A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

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Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention

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Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation

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Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

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Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans

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