DeepGrow

DeepGrow is a click based interactive segmentation model, where the user can guide the segmentation with positive and negative clicks. The positive clicks are intended to guide the segmentation towards the region of interest while the negative clicks are used for neglecting the background. It is based on prior work from Sakinis et. al 2019.

Deepgrow as a model can generalize to multiple imaging modalities such Magnetic Resonance Imaging, Computed Tomography etc.

It can also be adapted as an application in the MONAI Label framework. Here the user can directly leverage simultaneous training of the Deepgrow in the background and utilize it at the same time to annotate more samples to add to the training data pool.

Please note: Deepgrow training for both 2D & 3D is done on pairs of image & binary label mask data