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Extended Time-lapse Intravital Imaging of Real-time Multicellular Dynamics in the Tumor Microenvironment

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Cellflow: Advancing pathological image augmentation from spatial views to temporal trajectories.

Zeyu Liu¹, Tianyi Zhang², Yufang He¹

¹School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.

Medical Image Analysis

|February 18, 2026

Summary

This summary is machine-generated.

Cellflow, a novel temporal-aware generative framework, enhances pathological image analysis by modeling disease progression. This approach improves diagnostic accuracy and data augmentation for computational pathology tasks.

Keywords:

Data augmentation Generative diffusion model Pathology image analysis Temporal modeling

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Published on: June 12, 2016

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Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

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Published on: November 18, 2019

Area of Science:

Computational Pathology
Medical Image Analysis
Artificial Intelligence

Background:

Deep learning in pathology is limited by scarce annotated data for fine-grained tasks.
Existing spatial data augmentation methods lack morphological plausibility and temporal awareness.

Purpose of the Study:

Introduce Cellflow, the first temporal-aware generative framework for pathological image augmentation.
Model pathological transitions as biologically plausible temporal trajectories.

Main Methods:

Cellflow uses a stair-based diffusion bridge with classifier-guided probability-flow ODEs.
Generates intermediate states capturing cellular and tissue-level details.

Main Results:

Cellflow outperforms spatial augmentation and generative models across 7 diverse datasets.
Achieved improved classification performance, image fidelity, and temporal coherence.
Quantitative analysis validated the biological authenticity of generated transition sequences.

Conclusions:

Cellflow represents a paradigm shift in pathological data augmentation, moving from spatial to temporal modeling.
Enables robust model training, rare disease exploration, and educational simulations in computational pathology.