Use Cases
MONAI-label has mainly three use cases:
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Cold-start annotation: The user can annotate a dataset from scratch using basic viewer capabilities (i.e. brushes, DeepGrow)
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Interactive label editing or modification: The user can modify or edit annotations created by an automatic model.
- Model quality improvement: This is designed to take advantage of an expert budget (i.e. 5 hours per week of a radiologist) to improve the quality of a segmentation model. For this, there are several active learning techniques and base learner models that allow this to happen.
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DeepGrow App Based Use Case to Speed up Annotation Time: The below table shows how the annotation time at the user's end can be reduced as more and more volumes are annotated. As the user trains on the data that they have annotated, the total time taken at user's end to annotate a 3D volume reduces. The Spleen dataset from medical segmentation decathlon was used for this experiment. A similar MONAI Label DeepGrow app can be found here.
