Detecting cells and nuclei from different statinings with Faster R-CNN and Mask R-CNN in PyTorch 1.0

This project aims at providing a pipeline for efficient nuclei and cell detection from fluorescence images. It is based on facebookresearch maskrcnn benchmark, which is implemented in PyTorch 1.0. More information can be found at https://github.com/facebookresearch/maskrcnn-benchmark.

Detection of nuclei from rodent somatosensory cortex after DAPI-staining

Prediction of cells after poly-A staining

Here the labeled image.

Highlights of Maskrcnn benchmark

• PyTorch 1.0: RPN, Faster R-CNN and Mask R-CNN implementations that matches or exceeds Detectron accuracies
• Very fast: up to 2x faster than Detectron and 30% faster than mmdetection during training. See MODEL_ZOO.md for more details.
• Memory efficient: uses roughly 500MB less GPU memory than mmdetection during training
• Multi-GPU training and inference
• Batched inference: can perform inference using multiple images per batch per GPU
• CPU support for inference: runs on CPU in inference time. See our webcam demo for an example
• Provides pre-trained models for almost all reference Mask R-CNN and Faster R-CNN configurations with 1x schedule.

Installation

Check INSTALL.md for installation instructions.

Perform training on Nuclei dataset, further information in Tutorial_training.md

The tutorial (Tutorial_training.md) explains all the features including training, inference and prediction.

Abstractions

For more information on some of the main abstractions in our implementation, see ABSTRACTIONS.md.