Orateur
Description
The automatic detection of interstellar filaments in large-scale astrophysical surveys is a challenging segmentation problem for deep learning methods. Observations cover large portions of the Galactic plane and exhibit a wide range of densities and contrasts, spatially varying noise, and a heterogeneous distribution of structures. In addition, available annotations are necessarily incomplete, making the evaluation of segmentation models over the full observation particularly challenging.
LIS² (Large Image Split Segmentation) was developed to address these constraints through a dedicated workflow for deep learning on very large scientific images. The toolbox handles image splitting and preprocessing, data storage, model training and inference, as well as model evaluation through cross-validation. Particular attention is given to spatial partitioning: training and evaluation data are spatially separated to prevent overlap between regions while preserving the diversity of structures present in the observation. The K-fold framework then provides evaluation over several independent partitions, ensuring a robust comparison of model performances.
LIS² has been used to implement and compare segmentation architectures such as U-Net and its extensions on observations covering large portions of the Galactic plane. The framework also allows physical information to be incorporated into the models: encoding Galactic position in the network makes it possible to account for variations in the context along the Galactic plane and improves the detection of low-contrast structures.
The entire toolbox is modular and configurable, from data processing to training and inference, including models and evaluation procedures. This design makes it possible to adapt the workflow to the characteristics of the data and to the methodological choices of each problem while maintaining a reproducible experimental framework. LIS² provides a Research Software infrastructure for deep learning on large-scale scientific images, combining scalable data processing, unbiased spatial evaluation and configurable AI workflows.