  To automate the process of segmenting an anatomy of interest, we can learn a
model from previously annotated data. The learning-based approach uses
annotations to train a model that tries to emulate the expert labeling on a new
data set. While tremendous progress has been made using such approaches,
labeling of medical images remains a time-consuming and expensive task. In this
paper, we evaluate the utility of extreme points in learning to segment.
Specifically, we propose a novel approach to compute a confidence map from
extreme points that quantitatively encodes the priors derived from extreme
points. We use the confidence map as a cue to train a deep neural network based
on ResNet-101 and PSP module to develop a class-agnostic segmentation model
that outperforms state-of-the-art method that employs extreme points as a cue.
Further, we evaluate a realistic use-case by using our model to generate
training data for supervised learning (U-Net) and observed that U-Net performs
comparably when trained with either the generated data or the ground truth
data. These findings suggest that models trained using cues can be used to
generate reliable training data.
