Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment
  Anything Model for Crowd-Sourcing Medical Image Annotations
Pranav Kulkarni Adway Kanhere Dharmam Savani Andrew Chan Devina Chatterjee Paul H. Yi Vishwa S. Parekh
Curating annotations for medical image segmentation is a labor-intensive and
time-consuming task that requires domain expertise, resulting in "narrowly"
focused deep learning (DL) models with limited translational utility. Recently,
foundation models like the Segment Anything Model (SAM) have revolutionized
semantic segmentation with exceptional zero-shot generalizability across
various domains, including medical imaging, and hold a lot of promise for
streamlining the annotation process. However, SAM has yet to be evaluated in a
crowd-sourced setting to curate annotations for training 3D DL segmentation
models. In this work, we explore the potential of SAM for crowd-sourcing
"sparse" annotations from non-experts to generate "dense" segmentation masks
for training 3D nnU-Net models, a state-of-the-art DL segmentation model. Our
results indicate that while SAM-generated annotations exhibit high mean Dice
scores compared to ground-truth annotations, nnU-Net models trained on
SAM-generated annotations perform significantly worse than nnU-Net models
trained on ground-truth annotations ($p<0.001$, all).