Multiplex imaging is revolutionizing pathology by enabling the simultaneous visualization of multiple biomarkers within tissue samples, providing molecular-level insights that traditional hematoxylin and eosin (H&E) staining cannot provide. However, the complexity and cost of multiplex data acquisition have hindered its widespread adoption. Additionally, most existing large repositories of H&E images lack corresponding multiplex images, limiting opportunities for multi-modal analysis. To address these challenges, we leverage recent advances in latent diffusion models (LDMs), which excel at modeling complex data distributions by utilizing their powerful priors for fine-tuning to a target domain. In this paper, we introduce a novel framework for virtual multiplex staining that utilizes pretrained LDM parameters to generate multiplex images from H&E images using a conditional diffusion model. Our approach enables marker-by-marker generation by conditioning the diffusion model on each marker, while sharing the same architecture across all markers. To tackle the challenge of varying pixel value distributions across different marker stains and to improve inference speed, we fine-tune the model for single-step sampling, enhancing both color contrast fidelity and inference efficiency through pixel-level loss functions. We validate our framework on two publicly available datasets, notably demonstrating its effectiveness in generating up to 18 different marker types with improved accuracy, a substantial increase over the 2-3 marker types achieved in previous approaches. This validation highlights the potential of our framework, pioneering virtual multiplex staining. Finally, this paper bridges the gap between H&E and multiplex imaging, potentially enabling retrospective studies and large-scale analyses of existing H&E image repositories.

Neurons have a polarized structure, with dendrites and axons, and compartment-specific functions can be affected by the dwelling mitochondria. Recent studies have shown that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases. However, the conventional mitochondria analysis workflow mainly relies on manual annotations and generic image-processing software. Moreover, even though there have been recent developments in automatic mitochondria analysis using deep learning, the application of existing methods in a daily analysis remains challenging because the performance of a pretrained deep learning model can vary depending on the target data, and there are always errors in inference time, requiring human proofreading. To address these issues, we introduce MitoVis, a novel visualization system for end-to-end data processing and an interactive analysis of the morphology of neuronal mitochondria. MitoVis introduces a novel active learning framework based on recent contrastive learning, which allows accurate fine-tuning of the neural network model. MitoVis also provides novel visual guides for interactive proofreading so that users can quickly identify and correct errors in the result with minimal effort. We demonstrate the usefulness and efficacy of the system via case studies conducted by neuroscientists. The results show that MitoVis achieved up to 13.3× faster total analysis time in the case study compared to the conventional manual analysis workflow.