Instance Segmentation for Autonomous Segmentation of Scientific Images with Mask R-CNN
Total time needed: ~3 hours
Instance segmentation is an advanced form of segmentation which differentiates between individual objects of the same class. It is a useful technique when classical thresholding/watershed algorithms fail to segment individual instances due to overlap or unclear particle boundaries. This list will give a crash course on applying this technique to scientific images to measure populations of samples.
Potential Use Cases
Particle size distribution, detailed morphology information of each individual object, and resolving individual objects that may overlap.
BEGINNERScientists who are at least somewhat familiar with PyTorch.
Go through the following annotated itemsin order:
PAPER 1. Mask R-CNN
What contributions does Mask R-CNN make?
How well does Mask R-CNN perform?
What is the theory behind how Mask R-CNN works?
ARTICLE 2. Splash of Color Example: Adapting Mask R-CNN to New Datasets
How can I adapt Mask R-CNN to generate useful measurements for a new dataset?
How can I label my data so it can be used to train instance segmentation models?
How much data do I need to successfully train a model?
REPO 3. Detectron2
How do I implement Mask R-CNN?
Where can I find pre-trained instance segmentation models?