High-Quality Synthetic Annotated Tissue Data Using Conditional Generative Adversarial Networks

TY - GEN

T1 - High-Quality Synthetic Annotated Tissue Data Using Conditional Generative Adversarial Networks

AU - Srivastava, Siddharth

AU - Weijer, Cornelis Jan

AU - Bretschneider, Till

N1 - Publisher Copyright: © 2026 IEEE.

PY - 2026/5/20

Y1 - 2026/5/20

N2 - We present a deep-learning based pipeline for generating high-quality synthetic instance-segmentation datasets of tissues undergoing early gastrulation in chick embryo. We create point-clouds using the Lennard-Jones potential and learn an image-to-image translation from these point-clouds to create synthetic tissue images and auxiliary flow tensors using a Generative Adversarial Network, from which the segmentation masks are derived. We evaluate the downstream utility of our synthetic data by training Cellpose and Stardist models from scratch under data replacement and data augmentation scenarios, show that the synthetic datasets effectively capture the statistical properties of the real dataset, and show that our synthetic data improves segmentation performance on a held-out test set. This approach substantially reduces expert annotation time, as late-stage gastrulation data are challenging to acquire and manually label, while similar synthetic examples can be flexibly generated from easily obtainable and annotated early-stage data. Code: https://gitlab.com/siddharthsrivastava/synthetic-tissue-data.

AB - We present a deep-learning based pipeline for generating high-quality synthetic instance-segmentation datasets of tissues undergoing early gastrulation in chick embryo. We create point-clouds using the Lennard-Jones potential and learn an image-to-image translation from these point-clouds to create synthetic tissue images and auxiliary flow tensors using a Generative Adversarial Network, from which the segmentation masks are derived. We evaluate the downstream utility of our synthetic data by training Cellpose and Stardist models from scratch under data replacement and data augmentation scenarios, show that the synthetic datasets effectively capture the statistical properties of the real dataset, and show that our synthetic data improves segmentation performance on a held-out test set. This approach substantially reduces expert annotation time, as late-stage gastrulation data are challenging to acquire and manually label, while similar synthetic examples can be flexibly generated from easily obtainable and annotated early-stage data. Code: https://gitlab.com/siddharthsrivastava/synthetic-tissue-data.

KW - computational microscopy

KW - generative adversarial networks

KW - image synthesis

KW - segmentation

UR - https://www.scopus.com/pages/publications/105041628808

U2 - 10.1109/ISBI61048.2026.11515611

DO - 10.1109/ISBI61048.2026.11515611

M3 - Conference contribution

AN - SCOPUS:105041628808

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - ISBI 2026 - 23rd IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026

Y2 - 8 April 2026 through 11 April 2026

ER -