Improving Generation and Evaluation of Long Image Sequences for Embryo Development Prediction

Author:

Celard Pedro123ORCID,Seara Vieira Adrián123ORCID,Sorribes-Fdez José Manuel123ORCID,Iglesias Eva Lorenzo123ORCID,Borrajo Lourdes123ORCID

Affiliation:

1. Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain

2. CINBIO, Nanomaterials and Biomedical Research Centre, Universidade de Vigo, Campus Universitario Lagoas-Marcosende, 36310 Vigo, Spain

3. SING, Next Generation Computer Systems Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain

Abstract

Generating synthetic time series data, such as videos, presents a formidable challenge as complexity increases when it is necessary to maintain a specific distribution of shown stages. One such case is embryonic development, where prediction and categorization are crucial for anticipating future outcomes. To address this challenge, we propose a Siamese architecture based on diffusion models to generate predictive long-duration embryonic development videos and an evaluation method to select the most realistic video in a non-supervised manner. We validated this model using standard metrics, such as Fréchet inception distance (FID), Fréchet video distance (FVD), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE). The proposed model generates videos of up to 197 frames with a size of 128×128, considering real input images. Regarding the quality of the videos, all results showed improvements over the default model (FID = 129.18, FVD = 802.46, SSIM = 0.39, PSNR = 28.63, and MSE = 97.46). On the coherence of the stages, a global stage mean squared error of 9.00 was achieved versus the results of 13.31 and 59.3 for the default methods. The proposed technique produces more accurate videos and successfully removes cases that display sudden movements or changes.

Funder

Conselleria de Cultura, Educación e Universidade

Xunta de Galicia

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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