Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV

✍ Scribed by Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li

• Publisher: Springer Nature
• Year: 2022
• Tongue: English
• Leaves: 781
• Category: Library

✦ Synopsis

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

✦ Table of Contents

Preface
Organization
Contents – Part IV
Microscopic Image Analysis
An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided Attention
1 Introduction
2 Method
2.1 Overview
2.2 Grouping Guided Feature Interaction Module
2.3 Deep Assignment Module
3 Experiments
3.1 Datasets and Implementation Details
3.2 Results on Normal Karyotypes
3.3 Results on Karyotypes with Numerical Abnormalities
4 Conclusion
References
Efficient Biomedical Instance Segmentation via Knowledge Distillation
1 Introduction
2 Methodology
3 Experiments
3.1 Datasets and Metrics
3.2 Implementation Details
3.3 Experimental Results
4 Conclusion
References
Tracking by Weakly-Supervised Learning and Graph Optimization for Whole-Embryo C. elegans lineages
1 Introduction
2 Method
3 Results
4 Conclusion
References
Mask Rearranging Data Augmentation for 3D Mitochondria Segmentation
1 Introduction
2 Method
2.1 3D EM Image Generator
2.2 3D Mask Layout Generator
3 Experiments
3.1 Dataset and Experiment Settings
3.2 Experiments and Results
4 Conclusions
References
Semi-supervised Learning for Nerve Segmentation in Corneal Confocal Microscope Photography
1 Introduction
2 The Proposed Method
2.1 Pre-training
2.2 Model Fine-Tuning
2.3 Self-training
3 Evaluations
3.1 Data Set
3.2 Experimental Setup
3.3 Ablation Study
3.4 Comparisons with Semi-supervised Methods
4 Conclusions
References
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
1 Introduction
2 Method
2.1 Learning a Latent Space of Shapes
2.2 Neural Network Architecture
2.3 Data
3 Experiments and Results
3.1 Reconstruction of Cell Sequences
3.2 Generating New Cell Sequences
3.3 Temporal Interpolation
3.4 Generating Benchmarking Datasets
4 Discussion and Conclusion
References
Trichomonas Vaginalis Segmentation in Microscope Images
1 Introduction
2 Proposed Dataset
2.1 Data Collection
2.2 Dataset Features
3 Method
3.1 Overview
3.2 High-Resolution Fusion Module
3.3 Foreground-Background Attention Module
4 Experiments and Results
4.1 Experimental Settings
4.2 Comparison with State-of-the-Art
4.3 Ablation Study
5 Conclusion
References
NerveFormer: A Cross-Sample Aggregation Network for Corneal Nerve Segmentation
1 Introduction
2 Proposed Method
2.1 CNN Encoder
2.2 Deformable and External Attention Module (DEAM)
2.3 CNN Decoder
3 Experiments
3.1 Datasets and Implementation Details
3.2 Comparison with the State-of-the-Art Methods
3.3 Ablation Study
3.4 Conclusion
References
Domain Adaptive Mitochondria Segmentation via Enforcing Inter-Section Consistency
1 Introduction
2 Related Works
3 Method
4 Experiments
5 Conclusion
References
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-Sheet Microscopy
1 Introduction
2 Methods
2.1 Detecting of Corruption in Fourier Space
2.2 Formulating Stripe Removal as a Deep Unfolding Framework
2.3 Graph-Based Fourier Recovery Network G( )
2.4 Unfolded Hessian Prior for Structure Preservation H( )
2.5 Self2Self Denoising Loss Formulation
2.6 Competitive Methods
3 Results and Discussion
3.1 Evaluation on LSFM Images with Synthetic Stripe Artifact
3.2 Evaluation on LSFM Images with Real Stripe Artifact
4 Conclusion
References
End-to-End Cell Recognition by Point Annotation
1 Introduction
2 Methods
3 Experiments
3.1 Dataset Description and Experimental Settings
3.2 Experimental Results
4 Conclusion
References
ChrSNet: Chromosome Straightening Using Self-attention Guided Networks
1 Introduction
2 Methods
2.1 Curved Chromosome Synthesizer
2.2 ChrSNet: Chromosome Straightening Networks
3 Experiments
4 Conclusion
References
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
1 Introduction
2 Method
2.1 Baseline Framework
2.2 Region Proposal Rectification
3 Experiment
3.1 Datasets
3.2 Experimental Details
3.3 Evaluation Metric
4 Results and Discussion
5 Conclusion
References
DeepMIF: Deep Learning Based Cell Profiling for Multispectral Immunofluorescence Images with Graphical User Interface
1 Introduction
2 Materials
3 Methodology
3.1 Cell Detection and Classification on Deconvoluted Images
3.2 Markers Co-expression Identification
3.3 DeepMIF Graphical User Interface
4 Results and Discussion
References
Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction
1 Introduction
2 Related Work
3 Our Method: A Topology-Aware Loss
3.1 Assessing the Topology of Volumes
3.2 Loss Term Construction
4 Experiments
4.1 Training and Evaluation
4.2 Results
5 Discussion
References
Positron Emission Tomography
MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET
1 Introduction
2 Methods
2.1 Dataset and Pre-processing
2.2 Proposed Network
2.3 Training Details and Baseline Comparison
2.4 Evaluation Metrics
3 Results
3.1 Motion Simulation Test
3.2 Qualitative Analysis
3.3 Quantitative Analysis
4 Conclusion
References
PET Denoising and Uncertainty Estimation Based on NVAE Model Using Quantile Regression Loss
1 Introduction
2 Related Work
2.1 Variational Autoencoder (VAE)
2.2 Nouveau Variational Autoencoder (NVAE)
3 Methods
3.1 Overview
3.2 PET Image Denoising
3.3 Quantile Regression Loss
4 Experiment
4.1 Dataset
4.2 Data Analysis
5 Results
6 Discussion
7 Conclusion
References
TransEM: Residual Swin-Transformer Based Regularized PET Image Reconstruction
1 Introduction
2 Methods and Materials
2.1 Problem Formulation
2.2 Residual Swin-Transformer Regularizer
2.3 Implementation Details and Reference Methods
3 Experiment and Results
3.1 Experimental Evaluation
3.2 Results
3.3 Robustness Analysis
3.4 Ablation Study and Discussion
4 Conclusions
References
Supervised Deep Learning for Head Motion Correction in PET
1 Introduction
2 Methods
2.1 Data
2.2 Motion Correction Network Structure
2.3 Network Training Strategy
2.4 Motion Correction Inference
3 Results
3.1 Single Subject Pilot Experiments
3.2 Multi-subject Experiments
4 Discussion and Conclusion
References
Ultrasound Imaging
Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images
1 Introduction
2 Methods
2.1 Problem Setup
2.2 Training with Sampled 2D Slices from 3D Volumes
2.3 Fine-tuning with 2D Ultrasound Images
2.4 Inference
3 Experimental Design
4 Results and Discussion
4.1 Volume-Sampled Images
4.2 Native Freehand Images
5 Conclusion
References
Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography
1 Introduction
2 Method
2.1 Hook's Law and Poisson's Ratio
2.2 Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE)
2.3 Unsupervised Training
2.4 Data Collection
2.5 Network Architecture and Training Schedule
3 Results
3.1 Experimental Phantom Results
3.2 In Vivo results
3.3 Quantitative Results
4 Conclusions
References
Towards Unsupervised Ultrasound Video Clinical Quality Assessment with Multi-modality Data
1 Introduction
2 Related Work
3 Method
3.1 Model Structure
3.2 Objective Function
4 Experiment and Results
5 Conclusion
References
Key-frame Guided Network for Thyroid Nodule Recognition Using Ultrasound Videos
1 Introduction
2 Method
3 Experiments and Results
4 Conclusions
References
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis
1 Introduction
2 Related Work
3 Methodology
3.1 Hard Sample Discovery with Confidence Queue
3.2 Model Certainty Estimation with Certainty Queue
3.3 Loss Function of Adaptive Curriculum Learning
4 TNCD: Benchmark for Thyroid Nodule Classification
5 Experiment
5.1 Implementation and Evaluation Metric
5.2 Ablation Study and Schedule Analysis
5.3 Comparison with the State-of-the-arts
6 Conclusion
References
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
1 Introduction
2 Methods
2.1 Bayesian Shape Alignment
2.2 Locate-Net
3 Experiments
3.1 Dataset and Evaluation Metrics
3.2 Implementation Details
3.3 Comparison Baselines
3.4 Ablation Study
4 Conclusion
References
Uncertainty-aware Cascade Network for Ultrasound Image Segmentation with Ambiguous Boundary
1 Introduction
2 Methods
2.1 Architecture
2.2 Objective Function
3 Experiments
3.1 Comparison with State-of-the-Arts
3.2 Ablation Study
4 Conclusion
References
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
1 Introduction
2 Method
2.1 Landmark Regression Network
2.2 Dynamic Orientation Determination (DOD)
2.3 Scale Recovery
3 Experimental Setup
4 Results
5 Conclusions
References
Deep Motion Network for Freehand 3D Ultrasound Reconstruction
1 Introduction
2 Methodology
2.1 Temporal and Multi-branch Structure for IMU Fusion
2.2 Multi-modal Online Self-supervised Strategy
3 Experiments
4 Conclusion
References
Agent with Tangent-Based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound
1 Introduction
2 Method
2.1 Reinforcement Learning for Plane Localization
2.2 Auxiliary Task of State-Content Similarity Prediction
2.3 Imitation Learning Based Initialization
3 Experimental Result
3.1 Materials and Implementation Details
3.2 Quantitative and Qualitative Analysis
4 Conclusion
References
Weakly-Supervised High-Fidelity Ultrasound Video Synthesis with Feature Decoupling
1 Introduction
2 Methodology
2.1 Weakly-Supervised Training for Motion Estimation
2.2 Dual-Decoder Generator for Content and Texture Decoupling
2.3 Adversarial Learning and GAN Loss for Sharpness Improvement
3 Experiments and Results
4 Conclusions
References
Class Impression for Data-Free Incremental Learning
1 Introduction
2 Method
2.1 Class Impression
2.2 Novel Losses
3 Experiments
3.1 Datasets and Experimental Settings
3.2 Comparison with Baselines
3.3 Ablation Studies
4 Conclusion
References
Simultaneous Bone and Shadow Segmentation Network Using Task Correspondence Consistency
1 Introduction
2 Method
2.1 Preliminaries
2.2 Network Architecture
2.3 Cross Task Feature Transfer Block
2.4 Task Correspondence Consistency Loss
3 Experiments and Results
4 Discussion
5 Conclusion
References
Contrastive Learning for Echocardiographic View Integration
1 Introduction
2 Methods
2.1 Volume Contrastive Network
2.2 Volume Contrastive Losses
3 Experiments and Results
3.1 Experiment Settings
3.2 Results
4 Conclusion
References
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
1 Introduction
2 Method
2.1 Feature Extraction
2.2 Residual Transformer Module
2.3 3D Multi-Head Self-Attention
3 Experiments
4 Discussion
5 Conclusions
References
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
1 Introduction
2 Related Work
3 Methodology
3.1 EchoGNN Architecture
4 Experiments
4.1 Dataset
4.2 Implementation
4.3 Results and Discussion
4.4 Ablation Study
5 Limitations
6 Conclusion
References
EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography
1 Introduction
2 Related Works
3 Methods
3.1 Frames Sampling
3.2 Architecture Overview
3.3 Existing Methods for LVEF Estimation
4 Experiments
4.1 Datasets
4.2 Experimental Setup
5 Results
6 Discussion
7 Conclusion
References
Light-weight Spatio-Temporal Graphs for Segmentation and Ejection Fraction Prediction in Cardiac Ultrasound
1 Introduction
1.1 Prior Work
1.2 Contributions:
2 Method
2.1 Building Blocks
2.2 EchoGraphs - Left Ventricle Segmentation and EF Prediction
3 Experiments and Results
3.1 Dataset
3.2 Segmentation
3.3 EF Prediction
4 Discussion and Conclusion
References
Rethinking Breast Lesion Segmentation in Ultrasound: A New Video Dataset and A Baseline Network
1 Introduction
2 Method
2.1 Query and Memory Encoder
2.2 Parallel Spatial Temporal Transformer
2.3 Decoder
2.4 Dynamic Memory Selection
3 Experiments
3.1 Dataset and Implementation
3.2 Comparison with State-of-the-Art Methods
3.3 Ablation Study
4 Conclusion
References
MIRST-DM: Multi-instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
1 Introduction
2 Related Works
2.1 Adversarial Attacks and Defenses
2.2 Breast Ultrasound Image Classification
3 Proposed Method
3.1 Multi-instance RST
3.2 Drop-Max Layer
3.3 SimCLR Pretraining
4 Experimental Results
4.1 Experiment Setup
4.2 The Effectiveness of Multiple-Instance RST
4.3 The Effectiveness of the SimCLR Pretrained Model
4.4 The Effectiveness of the Drop-Max Layer
5 Conclusion
References
Towards Confident Detection of Prostate Cancer Using High Resolution Micro-ultrasound
1 Introduction
2 Materials and Methods
2.1 Data
2.2 Methodology
3 Experiments and Results
3.1 Effect of Co-teaching
3.2 Comparison of Uncertainty Methods
3.3 Model Demonstration
4 Conclusion
References
Video Data Analysis
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining
1 Introduction
2 Datasets
2.1 In-house US Dataset for Gallbladder Cancer
2.2 Public Lung US Dataset for COVID-19
3 Our Method
4 Experiments and Results
5 Conclusion
References
An Advanced Deep Learning Framework for Video-Based Diagnosis of ASD
1 Introduction
2 Methodology
2.1 Video Acquisition of Children
2.2 Advanced Deep Learning Framework
3 Experiments and Results
3.1 Implementation Details
3.2 Experimental Results
3.3 Analysis and Discussion
4 Conclusion
References
Automating Blastocyst Formation and Quality Prediction in Time-Lapse Imaging with Adaptive Key Frame Selection
1 Introduction
2 Method
2.1 Policy Network
2.2 Prediction Network
2.3 Loss Function
3 Experimental Results
4 Conclusions
References
Semi-supervised Spatial Temporal Attention Network for Video Polyp Segmentation
1 Introduction
2 Method
2.1 Temporal Local Context Attention
2.2 Proximity Frame Temporal-Spatial Attention
2.3 Loss Function
2.4 Training Flow
3 Experiments
3.1 Datasets and Implementation
3.2 Qualitative Evaluation
3.3 Quantitative Evaluation
3.4 Ablation Study
4 Conclusion
References
Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency
1 Introduction
2 Method
2.1 Self-supervised Learning
2.2 Dual-task Consistency Loss and Weight Mask
3 Experiments and Results
3.1 Datasets and Evaluation Metrics
3.2 Implementation Details
3.3 Comparison Results
3.4 Ablation Study
4 Discussion and Conclusions
References
Recurrent Implicit Neural Graph for Deformable Tracking in Endoscopic Videos
1 Introduction
2 Related Work
3 Methods
4 Experiments
5 Results
6 Conclusion
References
Pose-Based Tremor Classification for Parkinson's Disease Diagnosis from Video
1 Introduction
2 Method
2.1 Pose Extraction
2.2 Classification Network
3 Experiments
4 Conclusion
References
Image Segmentation I
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
1 Introduction
2 Method
2.1 Deep Implicit Surfaces
2.2 Neural Annotation Refinement
3 Datasets
3.1 Distorting a Golden Standard Segmentation Dataset
3.2 ALAN Dataset: A New 3D Dataset for Adrenal Gland Analysis
4 Experiments
4.1 Quantitative Experiments on Distorted Golden Standards
4.2 Adrenal Diagnosis on the Repaired ALAN Dataset
5 Conclusion
References
Few-shot Medical Image Segmentation Regularized with Self-reference and Contrastive Learning-10pt
1 Introduction
2 Methodology
2.1 Problem Setting
2.2 Local Prototype-Based Segmentation
2.3 Self-reference Regularization
2.4 Contrastive Learning
2.5 Superpixel-Based Self-supervised Learning
3 Experiments
3.1 Experimental Setup
3.2 Comparison with the State-of-the-Art (SOTA) Methods
3.3 Ablation Studies
4 Conclusion
References
Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency
1 Introduction
2 Methods
2.1 Modified Signed Distance Function (mSDF)
2.2 Dual Consistency Regularisation of Semi-Supervision
2.3 Differentiable vCDR estimation of Weakly Supervision
3 Experiments
3.1 Datasets and Implementation Details
4 Results
5 Conclusion
References
Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers
1 Introduction
2 Method
2.1 The Architecture of MeaFormer
2.2 Model Optimization
3 Experiments
4 Conclusions
References
DeSD: Self-Supervised Learning with Deep Self-Distillation for 3D Medical Image Segmentation
1 Introduction
2 Method
2.1 Overview
2.2 Deep Self-Distillation
2.3 Downstream Transfer Learning
2.4 Architecture Details
3 Experiments and Results
3.1 Datasets and Evaluation Metrics
3.2 Implementation Details
3.3 Results
4 Conclusion
References
Self-supervised 3D Anatomy Segmentation Using Self-distilled Masked Image Transformer (SMIT)
1 Introduction
2 Method
3 Experiments and Results
4 Discussion and Conclusion
References
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method
1 Introduction
2 Method
2.1 DeepRecon Architecture
2.2 Learning of DeepRecon
2.3 3D Reconstruction and 4D Motion Adaptation
3 Experiments
3.1 Latent-space-based 2D Segmentation
3.2 3D Volume Reconstruction
3.3 Motion Pattern Adaptation
4 Conclusion
References
Online Easy Example Mining for Weakly-Supervised Gland Segmentation from Histology Images
1 Introduction
2 Method
2.1 Overall Framework
2.2 Online Easy Example Mining
2.3 Network Training
3 Experiments
3.1 Dataset
3.2 Compare with State-of-the-Arts
3.3 Ablation Study
4 Conclusion
References
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
1 Introduction
2 Joint Class-Affinity Segmentation Framework
2.1 Differentiated Affinity Reasoning (DAR)
2.2 Class-Affinity Loss Correction (CALC)
3 Experiments
4 Conclusion
References
Task-Relevant Feature Replenishment for Cross-Centre Polyp Segmentation
1 Introduction
2 Methodology
2.1 Domain-Invariant Feature Decomposition (DIFD)
2.2 Task-Relevant Feature Replenishment
2.3 Polyp-Aware Adversarial Learning (PAAL)
2.4 Overall Network Training
3 Experiments
3.1 Experimental Settings
3.2 Comparison with State-of-the-Arts
3.3 Ablation Study
4 Conclusion
References
Vol2Flow: Segment 3D Volumes Using a Sequence of Registration Flows
1 Introduction
1.1 Related Works
2 Method
2.1 Vol2Flow Network and Self-Supervised Learning
2.2 Mask Propagation
3 Experimental Setup
4 Results and Discussion
5 Conclusion
References
Parameter-Free Latent Space Transformer for Zero-Shot Bidirectional Cross-modality Liver Segmentation
1 Introduction
2 Method
3 Experiments and Results
4 Conclusion
References
Using Guided Self-Attention with Local Information for Polyp Segmentation
1 Introduction
2 Method
3 Experiments
3.1 Experiments on Polyp Segmentation
3.2 Ablation Study
4 Conclusion
References
Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
1 Introduction
1.1 Overview
1.2 Unsupervised Contrastive Learning
2 Experiments
3 Conclusion
A Hardness-aware Property of the Contrastive Losses
References
Context-Aware Voxel-Wise Contrastive Learning for Label Efficient Multi-organ Segmentation
1 Introduction
2 Method
2.1 Supervised Losses for Labeled Voxels
2.2 Context-Aware Contrastive Learning Loss for Unlabeled Voxels
2.3 Overall Loss Function
2.4 Implementation Details
3 Experiments
3.1 Dataset
3.2 Comparing to State-of-the-Art Methods
3.3 Ablation Study
4 Conclusion
References
Vector Quantisation for Robust Segmentation-10pt
1 Introduction
1.1 Contribution
2 Methods
2.1 Robustness and Network Assumptions
2.2 Quantisation for Robustness
2.3 Perturbation Bounds
2.4 Implementation Details and Data
3 Experiments
3.1 Codebook Study
3.2 Domain Shift Study
3.3 Perturbation Study
4 Conclusion
References
A Hybrid Propagation Network for Interactive Volumetric Image Segmentation
1 Introduction
2 Methodology
2.1 Overview
2.2 Volume Propagation Network
2.3 Slice Propagation Network
2.4 Implementation
3 Experiments
3.1 Datasets and Experimental Setup
3.2 Comparison with Previous Methods
3.3 Ablation Study
3.4 User Study
4 Conclusion
References
SelfMix: A Self-adaptive Data Augmentation Method for Lesion Segmentation*-8pt
1 Introduction
2 Method
2.1 Self-adaptive Data Augmentation
3 Experiments
3.1 Datasets and Implementation Details
3.2 Experimental Results
3.3 Effectiveness Analysis Using SelfMix:
3.4 Relationship with Mixup, CutMix and CarveMix
4 Conclusion
References
Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks
1 Introduction
2 Method
3 Experiments
3.1 Data
3.2 Encoding Configuration
3.3 Results
3.4 Ablation Study
4 Discussion
5 Conclusions
References
Transforming the Interactive Segmentation for Medical Imaging
1 Introduction
2 Methods
2.1 Problem Scenario
2.2 Encoder (ENC)
2.3 Refinement (REF)
3 Experiments
3.1 Datasets
3.2 Evaluation Metrics
3.3 Implementation Details
4 Results
4.1 User Interactions Simulation
4.2 Comparisons with State-of-the-Art
4.3 Ablation Study
4.4 Visualization of Results
4.5 Visualization of the Interaction Process
5 Conclusion
References
Learning Incrementally to Segment Multiple Organs in a CT Image
1 Introduction
2 Related Work
3 Method
3.1 IL for MOS
3.2 Memory Module
4 Experiments
4.1 Setup
4.2 Results and Discussions
5 Conclusion
References
Harnessing Deep Bladder Tumor Segmentation with Logical Clinical Knowledge
1 Introduction
2 Method
2.1 Constructing Logic Rules for Bladder Tumor Localization
2.2 Embedding Logic Rules into Latent Features
2.3 Optimization of Segmentation Network with Logic Rules
3 Experiments
3.1 Test of Segmentation Enhancement by Logic Rules
3.2 Comparison with Other Segmentation Methods
4 Conclusion
References
Test-Time Adaptation with Shape Moments for Image Segmentation
1 Introduction
2 Method
3 Experiments
3.1 Test-time Adaptation with Shape Descriptors
3.2 Results and Discussion
4 Conclusion
References
Author Index