Medical Optical Imaging and Virtual Microscopy Image Analysis

This book constitutes the refereed proceedings of the Second International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2024, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.

The 21 regular papers presented at MOVI 2024 were carefully reviewed and selected from 29 submissions.

They are grouped into these two topical sections: Medical Optical Imaging and Virtual Microscopy Image Analysis and Kidney Pathology Image segmentation (KPIs) Challenge.

Inhalt

Medical Optical Imaging and Virtual Microscopy Image Analysis: A Deployable Microscopic Image Segmentation Look-Up Table Based on A Dilated CNN.- From Feature Maps to Few-Shot Cell Segmentation.- Deep Learning for Classifying Anti-Shigella Opsono-phagocytosis-promoting Monoclonal Antibodies.- Multi-target Stain Normalization for Histology Slides.- Intensity Inhomogeneity Correction for Large Panoramic Electron Microscopy Images.- Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging.- Lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides.- TRP-Net: Transformer with RMM and PPM for High-efficiency Circulating Abnormal Cells Detection in Multichannel Fluorescence Imaging.- Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals.- Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution.- CLSMI2T3: 3D CLSM Vasculature Volume Reconstruction from A Single 2D Slice by Off-Focal Plane Signal Using Synthetic Data.- Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging.- MDSN: Multi-stage Context-Aware Nuclei Detection-Segmentation Network.- Structured Model Pruning for Efficient Inference in Computational Pathology.- Histopathology Image Embedding based on Foundation Models Features Aggregation for DLBCL Patient Treatment Response Prediction.- EM-Compressor: Electron Microscopy Image Compression in Connectomics with Variational Autoencoders. Kidney Pathology Image segmentation (KPIs) Challenge: AC-UNet: A Self-Adaptive Cropping Approach for Kidney Pathology Image Segmentation.- SAM-Glomeruli: Enhanced Segment Anything Model for Precise Glomeruli Segmentation.- A Robust Deep Learning Method for WSI-level Diseased Glomeruli Segmentation.- Ensembled SegNeXt Based Glomeruli Segmentation.- Glomeruli Segmentation in Whole-Slide Images: Is Better Local Performance Always Better.