An expert-driven data generation pipeline for histological images
This addresses the problem of limited annotated data in medical imaging for researchers and practitioners, though it is incremental as it builds on existing synthetic data generation methods.
The paper tackles the scarcity of annotated histological images for deep learning by proposing a pipeline that generates synthetic datasets from a few annotated images, enabling effective training of instance segmentation models with realistic cell shapes and placements.
Deep Learning (DL) models have been successfully applied to many applications including biomedical cell segmentation and classification in histological images. These models require large amounts of annotated data which might not always be available, especially in the medical field where annotations are scarce and expensive. To overcome this limitation, we propose a novel pipeline for generating synthetic datasets for cell segmentation. Given only a handful of annotated images, our method generates a large dataset of images which can be used to effectively train DL instance segmentation models. Our solution is designed to generate cells of realistic shapes and placement by allowing experts to incorporate domain knowledge during the generation of the dataset.