Clinical Image-Based Procedures : 11th Workshop, CLIP 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Preface Organization Contents Fast Auto-differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging 1 Introduction 2 Methods 2.1 DRR Generation 2.2 Siddon's Method and Its GPU Extensions 2.3 Vectorizing Siddon's Method 2.4 Differentiating DRRs with Respect to Imaging Parameters 3 Experiments 3.1 Performance Analysis 3.2 DRR-Based Gradient Descent for Slice-to-Volume Registration 4 Discussion References Multi-channel Residual Neural Network Based on Squeeze-and-Excitation for Osteoporosis Diagnosis 1 Introduction 2 Methodology 2.1 Multi-channel Residual Network Module 2.2 Squeeze-Excitation Attention Unit 2.3 Classification Block 3 Experimental Results 3.1 Datasets and Preprocessing 3.2 Experiment Setup 3.3 Results 4 Conclusion References Machine Learning Based Approach for Motion Detection and Estimation in Routinely Acquired Low Resolution Near Infrared Fluorescence Optical Imaging 1 Introduction and Motivation 2 Background 2.1 Related Work 2.2 Participants 2.3 Data 3 Method 3.1 Before FI 3.2 After FI 4 Results 5 Discussion and Future Work References Automatic Landmark Identification on IntraOralScans 1 Introduction 2 Related Work 3 Method 3.1 Data 3.2 Pre-processing 3.3 Rendering 2D Views 3.4 Training 3.5 Prediction 4 Results 5 Discussion 6 Conclusion References STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation 1 Introduction 2 Methods 2.1 STAU-Net 2.2 Structure Attention Fusion Block 3 Experiments and Results 3.1 Dataset 3.2 Implementation Details 3.3 Evaluation Metrics 3.4 Result 4 Conclusion References Convolutional Redistribution Network for Multi-view Medical Image Diagnosis 1 Introduction 2 Related Work 3 Method 3.1 The Workflow of Convolutional Redistribute Network 3.2 Convolutional Redistribute Network 3.3 Loss Function 4 Experiment 4.1 Experiment on Pancreatic Tumor Classification 4.2 Experiment on Standard Datasets 5 Conclusion References Feature Patch Based Attention Model for Dental Caries Classification 1 Introduction 2 Related Work 3 Methodology 3.1 Feature Patch Extraction 3.2 Feature Patch Attention 3.3 Loss Function 4 Experiments and Analysis 4.1 Dataset and Settings 4.2 Experimental Results 5 Conclusion References Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition 1 Introduction 2 Materials 3 Methods 4 Results 4.1 The Best Distribution Difference Metric 4.2 Source Domain Selection Strategy 4.3 Verifying the Source Domain Selection Strategy 5 Discussion 6 Conclusion References Automated Cone and Vessel Analysis in Adaptive Optics Like Retinal Images for Clinical Diagnostics Support 1 Introduction 2 Methods 2.1 Requirements and Data Specifics 2.2 Vessel Segmentation and Analysis 2.3 Cone Analysis 3 Evaluation and Results 4 Discussion and Conclusion References Author Index This book constitutes the proceedings of the 11th Workshop on Clinical Image-Based Procedures, CLIP 2022, which was held in conjunction with MICCAI 2022, in Singapore in September 2022. The 9 full papers included in this book were carefully reviewed and selected from 12 submissions. They focus on the applicability of basic research methods in the clinical practice by creating holistic patient models as an important step towards personalized healthcare.