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Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Cover of 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2018'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning
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    Chapter 2 A Combined Simulation and Machine Learning Approach for Image-Based Force Classification During Robotized Intravitreal Injections
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    Chapter 3 Learning from Noisy Label Statistics: Detecting High Grade Prostate Cancer in Ultrasound Guided Biopsy
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    Chapter 4 A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
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    Chapter 5 Soft-Body Registration of Pre-operative 3D Models to Intra-operative RGBD Partial Body Scans
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    Chapter 6 Automatic Classification of Cochlear Implant Electrode Cavity Positioning
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    Chapter 7 X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
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    Chapter 8 Endoscopic Navigation in the Absence of CT Imaging
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    Chapter 9 A Novel Mixed Reality Navigation System for Laparoscopy Surgery
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    Chapter 10 Respiratory Motion Modelling Using cGANs
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    Chapter 11 Physics-Based Simulation to Enable Ultrasound Monitoring of HIFU Ablation: An MRI Validation
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    Chapter 12 DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
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    Chapter 13 Exploiting Partial Structural Symmetry for Patient-Specific Image Augmentation in Trauma Interventions
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    Chapter 14 Intraoperative Brain Shift Compensation Using a Hybrid Mixture Model
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    Chapter 15 Video-Based Computer Aided Arthroscopy for Patient Specific Reconstruction of the Anterior Cruciate Ligament
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    Chapter 16 Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
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    Chapter 17 Endoscopic Laser Surface Scanner for Minimally Invasive Abdominal Surgeries
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    Chapter 18 Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging
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    Chapter 19 Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network
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    Chapter 20 An Open Framework Enabling Electromagnetic Tracking in Image-Guided Interventions
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    Chapter 21 Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks
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    Chapter 22 Towards Automatic Report Generation in Spine Radiology Using Weakly Supervised Framework
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    Chapter 23 A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science
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    Chapter 24 Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis
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    Chapter 25 Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks
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    Chapter 26 Needle Tip Force Estimation Using an OCT Fiber and a Fused convGRU-CNN Architecture
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    Chapter 27 Fast GPU Computation of 3D Isothermal Volumes in the Vicinity of Major Blood Vessels for Multiprobe Cryoablation Simulation
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    Chapter 28 A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery
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    Chapter 29 Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification
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    Chapter 30 Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
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    Chapter 31 DeepPhase: Surgical Phase Recognition in CATARACTS Videos
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    Chapter 32 Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy Using Deep Learning
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    Chapter 33 Unsupervised Learning for Surgical Motion by Learning to Predict the Future
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    Chapter 34 Volumetric Clipping Surface: Un-occluded Visualization of Structures Preserving Depth Cues into Surrounding Organs
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    Chapter 35 Closing the Calibration Loop: An Inside-Out-Tracking Paradigm for Augmented Reality in Orthopedic Surgery
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    Chapter 36 Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
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    Chapter 37 Simultaneous Surgical Visibility Assessment, Restoration, and Augmented Stereo Surface Reconstruction for Robotic Prostatectomy
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    Chapter 38 Real-Time Augmented Reality for Ear Surgery
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    Chapter 39 Framework for Fusion of Data- and Model-Based Approaches for Ultrasound Simulation
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    Chapter 40 Esophageal Gross Tumor Volume Segmentation Using a 3D Convolutional Neural Network
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    Chapter 41 Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation
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    Chapter 42 Learn the New, Keep the Old: Extending Pretrained Models with New Anatomy and Images
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    Chapter 43 ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation
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    Chapter 44 MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation
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    Chapter 45 How to Exploit Weaknesses in Biomedical Challenge Design and Organization
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    Chapter 46 Accurate Weakly-Supervised Deep Lesion Segmentation Using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
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    Chapter 47 Semi-automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
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    Chapter 48 A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation
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    Chapter 49 3D U-JAPA-Net: Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation
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    Chapter 50 Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
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    Chapter 51 Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
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    Chapter 52 Segmentation of Renal Structures for Image-Guided Surgery
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    Chapter 53 Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes
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    Chapter 54 Local and Non-local Deep Feature Fusion for Malignancy Characterization of Hepatocellular Carcinoma
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    Chapter 55 A Novel Bayesian Model Incorporating Deep Neural Network and Statistical Shape Model for Pancreas Segmentation
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    Chapter 56 Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks
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    Chapter 57 Generalizing Deep Models for Ultrasound Image Segmentation
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    Chapter 58 Inter-site Variability in Prostate Segmentation Accuracy Using Deep Learning
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    Chapter 59 Deep Learning-Based Boundary Detection for Model-Based Segmentation with Application to MR Prostate Segmentation
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    Chapter 60 Deep Attentional Features for Prostate Segmentation in Ultrasound
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    Chapter 61 Accurate and Robust Segmentation of the Clinical Target Volume for Prostate Brachytherapy
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    Chapter 62 Hashing-Based Atlas Ranking and Selection for Multiple-Atlas Segmentation
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    Chapter 63 Corners Detection for Bioresorbable Vascular Scaffolds Segmentation in IVOCT Images
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    Chapter 64 The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
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    Chapter 65 Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images
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    Chapter 66 Real-Time Prediction of Segmentation Quality
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    Chapter 67 Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations
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    Chapter 68 Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension
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    Chapter 69 Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images
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    Chapter 70 Left Ventricle Segmentation via Optical-Flow-Net from Short-Axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion
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    Chapter 71 VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-Voxel Discrimination
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    Chapter 72 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
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    Chapter 73 Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound
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    Chapter 74 Btrfly Net: Vertebrae Labelling with Energy-Based Adversarial Learning of Local Spine Prior
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    Chapter 75 AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation
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    Chapter 76 CFCM: Segmentation via Coarse to Fine Context Memory
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    Chapter 77 Pyramid-Based Fully Convolutional Networks for Cell Segmentation
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    Chapter 78 Automated Object Tracing for Biomedical Image Segmentation Using a Deep Convolutional Neural Network
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    Chapter 79 RBC Semantic Segmentation for Sickle Cell Disease Based on Deformable U-Net
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    Chapter 80 Accurate Detection of Inner Ears in Head CTs Using a Deep Volume-to-Volume Regression Network with False Positive Suppression and a Shape-Based Constraint
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    Chapter 81 Automatic Teeth Segmentation in Panoramic X-Ray Images Using a Coupled Shape Model in Combination with a Neural Network
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    Chapter 82 Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning
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    Chapter 83 Automatic Skin Lesion Segmentation on Dermoscopic Images by the Means of Superpixel Merging
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    Chapter 84 Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation
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    Chapter 85 Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
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    Chapter 86 Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes
Attention for Chapter 25: Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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11 X users

Citations

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30 Dimensions

Readers on

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105 Mendeley
Summary X Dimensions citations
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Chapter title
Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks
Chapter number 25
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-00937-3_25
Book ISBNs
978-3-03-000936-6, 978-3-03-000937-3
Authors

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller, Ismail Fawaz, Hassan, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 23%
Student > Master 10 10%
Student > Doctoral Student 9 9%
Researcher 8 8%
Student > Bachelor 8 8%
Other 14 13%
Unknown 32 30%
Readers by discipline Count As %
Computer Science 33 31%
Engineering 21 20%
Medicine and Dentistry 12 11%
Neuroscience 1 <1%
Design 1 <1%
Other 0 0%
Unknown 37 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 September 2018.
All research outputs
#4,413,461
of 24,002,307 outputs
Outputs from arXiv
#104,451
of 1,011,770 outputs
Outputs of similar age
#77,835
of 314,512 outputs
Outputs of similar age from arXiv
#2,708
of 24,438 outputs
Altmetric has tracked 24,002,307 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,011,770 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 314,512 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 24,438 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.

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