Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 : 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part I / edited by Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert.

Format:

Book

Contributor:

Bruijne, Marleen de, Editor.

Cattin, Philippe C., Editor.

Cotin, Stéphane, Editor.

Padoy, Nicolas., Editor.

Speidel, Stefanie, Editor.

Zheng, Yefeng., Editor.

Essert, Caroline, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12901

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12901

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer engineering.

Computer networks.

Bioinformatics.

Pattern recognition systems.

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Computational and Systems Biology.

Automated Pattern Recognition.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Computational and Systems Biology.

Automated Pattern Recognition.

Physical Description:

1 online resource (XXXVII, 746 pages) : 252 illustrations

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

Contents:

Image Segmentation

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

Pancreas CT Segmentation by Predictive Phenotyping

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting

Convolution-Free Medical Image Segmentation using Transformer Networks

Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks

A Multi-Branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

Automatic Polyp Segmentation via Multi-scale Subtraction Network

Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance

Progressively Normalized Self-Attention Network for Video Polyp Segmentation

SGNet: Structure-aware Graph-based Network for Airway Semantic Segmentation

NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale

AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

Boundary-aware Transformers for Skin Lesion Segmentation

A Topological-Attention ConvLSTM Network and Its Application to EM Images

BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation

Partial-supervised Learning for Vessel Segmentation in Ocular Images

Unsupervised Network Learning for Cell Segmentation

MT-UDA: Towards Unsupervised Cross-Modality Medical Image Segmentation with Limited Source Labels

Context-aware virtual adversarial training for anatomically-plausible segmentation

Interactive segmentation via deep learning and B-spline explicit active surfaces

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation

Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography

Coarse-to-fine Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy

Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-branch Multi-scale Attention Network

A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

Comprehensive Importance-based Selective Regularization for Continual Segmentation Across Multiple Sites

ReSGAN: Intracranial Hemorrhage Segmentation with Residuals of Synthetic Brain CT Scans

Refined Local-imbalance-based Weight for Airway Segmentation in CT

Selective Learning from External Data for CT Image Segmentation

Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT

MouseGAN: GAN-Based Multiple MRI Modalities Synthesis and Segmentation for Mouse Brain Structures

Style Curriculum Learning for Robust Medical Image Segmentation

Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition

Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image

Learning to Address Intra-segment Misclassification in Retinal Imaging

Flip Learning: Erase to Segment

DC-Net: Dual Context Network for 2D Medical Image Segmentation

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation

A hybrid attention ensemble framework for zonal prostate segmentation

3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation

HRENet: A Hard Region Enhancement Network for Polyp Segmentation

A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images

TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation

Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation

Hybrid graph convolutional neural networks for anatomical segmentation

RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation

CCBANet: Cascading Context and Balancing Attention for Polyp Segmentation

Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation

TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection

Distilling effective supervision for robust medical image segmentation with noisy labels

On the relationship between calibrated predictors and unbiased volume estimation

High-resolution segmentation of lumbar vertebrae from conventional thick slice MRI

Shallow Attention Network for Polyp Segmentation

A Line to Align: Deep Dynamic Time Warping for Retinal OCT Segmentation

Learnable Oriented-Derivative Network for Polyp Segmentation

LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images.

Other Format:

Printed edition:

ISBN:

978-3-030-87193-2

9783030871932

Access Restriction:

Restricted for use by site license.