FLoRIN¶
FLoRIN, the Flexible Learning-free Reconstruction of Neural Volumes pipeline, is a pipeline for large-scale parallel and distributed computer vision. Offering easy setup and access to hierarchical parallelism, FLorIN is ideal for scaling computer vision to HPC systems.
Originally, this project was our response to the question of how to segment and reconstruct neural microscopy (e.g., micro-CT tomography, low-resolution electron microscopy, fluorescence microscopy etc.) without large amounts of training data available to train a neural network. We tackled this problem by revisiting classical computer vision methods, eventually developing the N-Dimensional Neighborhood Thresholding (NDNT) algorithm as a modern update to integral image-based thresholding. FLoRIN has since been shown to be a fast, robust segmentation and reconstruction engine across different imaging modalities and datasets.
This package implements the NDNT algorithm, as well as a straightforward API for mixed serial, parallel, and distributed computer vision. These docs provide examples of how to use FLoRIN with various mixtures of serial and parallel processing and how to customize the FLoRIN pipeline with new functions and features.
Publications¶
- Shahbazi, Ali, Jeffery Kinnison, Rafael Vescovi, Ming Du, Robert Hill, Maximilian Jösch, Marc Takeno et al. “Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.” Scientific reports 8, no. 1 (2018): 14247.