Medical image computing and computer assisted intervention -- MICCAI 2023 : 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings. Part IV

Medical image computing and computer assisted intervention -- MICCAI 2023 : Part IV / 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings. Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, editors.

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023....

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Bibliographic Details
Corporate Author: International Conference on Medical Image Computing and Computer-Assisted Intervention Vancouver, B.C. ; Online
Other Authors: Greenspan, Hayit (Editor), Madabhushi, Anant (Editor), Mousavi, Parvin (Editor), Salcudean, Septimiu Edmund (Editor), Duncan, James, 1951- (Editor), Syeda-Mahmood, Tanveer (Editor), Taylor, Russell (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, 2023.
Series:Lecture notes in computer science ; 14223.
Subjects:
Diagnostic imaging > Data processing > Congresses.
Diagnostic imaging > Data processing
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents - Part IV
  • Image Segmentation II
  • Category-Level Regularized Unlabeled-to-Labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data
  • 1 Introduction
  • 2 Methods
  • 2.1 Problem Formulation and Basic Architecture
  • 2.2 Pseudo Labeling for Local Distribution Fitting
  • 2.3 Category-Level Regularized Unlabeled-to-Labeled Learning
  • 3 Experiments and Results
  • 4 Conclusion
  • References
  • Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation
  • 1 Introduction
  • 2 Method
  • 2.1 Problem Definition and Method Overview
  • 2.2 Style Augmentation
  • 2.3 Contrastive Feature Disentanglement
  • 2.4 Training and Inference
  • 3 Experiments and Results
  • 4 Conclusion
  • References
  • Transformer-Based Annotation Bias-Aware Medical Image Segmentation
  • 1 Introduction
  • 2 Method
  • 2.1 Problem Formalization and Method Overview
  • 2.2 CNN Encoder
  • 2.3 PFE Module
  • 2.4 SS Head
  • 2.5 Loss and Inference
  • 3 Experiments and Results
  • 3.1 Dataset and Experimental Setup
  • 3.2 Comparative Experiments
  • 3.3 Ablation Analysis
  • 4 Conclusion
  • References
  • Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation
  • 1 Introduction
  • 2 Method
  • 2.1 Uncertainty Estimation for Classification and Segmentation
  • 2.2 Uncertainty-Informed Mutual Learning
  • 2.3 Mutual Learning Process
  • 3 Experiments
  • 4 Conclusion
  • References
  • A General Stitching Solution for Whole-Brain 3D Nuclei Instance Segmentation from Microscopy Images
  • 1 Introduction
  • 2 Methods
  • 2.1 Graph Contextual Model
  • 2.2 Hierarchical Stitching Framework for Whole-Brain NIS
  • 2.3 Implementation Details
  • 3 Experiments
  • 3.1 Experimental Settings
  • 3.2 Evaluation Metrics
  • 3.3 Evaluating the Accuracy of NIS Stitching Results
  • 3.4 Whole-Brain NIS in Neuroscience Applications
  • 4 Conclusion
  • References
  • Adult-Like Phase and Multi-scale Assistance for Isointense Infant Brain Tissue Segmentation
  • 1 Introduction
  • 2 Methods
  • 2.1 Semantics-Preserved Multi-phase Synthesis
  • 2.2 Transformer-Based Multi-scale Segmentation
  • 3 Experiments and Results
  • 3.1 Dataset and Evaluation Metrics
  • 3.2 Implementation Details
  • 3.3 Evaluation and Discussion
  • 4 Conclusion
  • References
  • Robust Segmentation via Topology Violation Detection and Feature Synthesis
  • 1 Introduction
  • 2 Method
  • 3 Evaluation
  • 4 Conclusion
  • References
  • GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
  • 1 Introduction
  • 2 Methodology
  • 2.1 The Overall Framework
  • 2.2 Multi-view Global-Local Fusion Module
  • 2.3 Dense Cycle Loss
  • 3 Experiment
  • 3.1 Comparison with the State-of-the-Art Methods
  • 3.2 Ablation Study
  • 4 Conclusion
  • References

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