Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain

2026-05-21

AI Summary

This work addresses the scarcity of annotated biomedical video data, which severely limits the performance of deep cell tracking models, compounded by the high cost of manual annotation. To overcome this challenge, the study introduces a novel approach that, for the first time, models the temporal evolution of cell contours in the Elliptic Fourier Descriptor (EFD) domain. By reformulating cell phantom generation as a multivariate time series generation task on EFD coefficients, the method incorporates strong morphological priors to guarantee geometric plausibility and temporal coherence. This enables efficient synthesis of biologically realistic cell tracking videos that can be seamlessly integrated into data augmentation pipelines, substantially reducing the annotation burden associated with constructing new datasets.

Abstract

Training Deep Neural Networks for tracking individual cells in biomedical videos requires a large amount of annotated data. The annotation of videos for cell tracking is very time consuming and often requires domain expertise; this explains the limited availability of public annotated data to address important medical problems like tissue repair or cancer treatment. Generating synthetic videos along with their Ground Truth annotations is a promising solution that relies, as a foundational first step, on the synthesis of single cell annotations (or phantoms). Phantoms need to be time consistent, as they have to replicate biological processes that are specific to the cell types. In this work, we propose a novel framework for generating videos of cell phantoms in the Elliptical Fourier Descriptors (EFDs) domain, a compact and geometrically interpretable representation for 2D closed contours. We represent the cell phantom evolution as a multivariate time series of EFD coefficients, introducing a strong prior for cell morphology and enabling the efficient generation of sequences that evolve coherently in time. Our experimental validation proves that modelling the temporal evolution in EFD space enables the generation of biologically plausible phantom videos. Our method can be used in generative pipelines for synthesizing annotated data for cell tracking, thus strongly mitigating the annotation effort for creating new datasets. Our code is available for download here: https://github.com/FrancescoBenedetto99/efd-cell-video-gen.

Problem

• cell tracking
• annotated data
• synthetic video generation
• cell phantom
• biomedical videos

Innovation

• Elliptical Fourier Descriptors
• cell phantom generation
• time-consistent synthesis
• cell tracking
• synthetic biomedical video