  Segmentation of organs or lesions from medical images plays an essential role
in many clinical applications such as diagnosis and treatment planning. Though
Convolutional Neural Networks (CNN) have achieved the state-of-the-art
performance for automatic segmentation, they are often limited by the lack of
clinically acceptable accuracy and robustness in complex cases. Therefore,
interactive segmentation is a practical alternative to these methods. However,
traditional interactive segmentation methods require a large amount of user
interactions, and recently proposed CNN-based interactive segmentation methods
are limited by poor performance on previously unseen objects. To solve these
problems, we propose a novel deep learning-based interactive segmentation
method that not only has high efficiency due to only requiring clicks as user
inputs but also generalizes well to a range of previously unseen objects.
Specifically, we first encode user-provided interior margin points via our
proposed exponentialized geodesic distance that enables a CNN to achieve a good
initial segmentation result of both previously seen and unseen objects, then we
use a novel information fusion method that combines the initial segmentation
with only few additional user clicks to efficiently obtain a refined
segmentation. We validated our proposed framework through extensive experiments
on 2D and 3D medical image segmentation tasks with a wide range of previous
unseen objects that were not present in the training set. Experimental results
showed that our proposed framework 1) achieves accurate results with fewer user
interactions and less time compared with state-of-the-art interactive
frameworks and 2) generalizes well to previously unseen objects.
