  Image segmentation plays an essential role in medicine for both diagnostic
and interventional tasks. Segmentation approaches are either manual,
semi-automated or fully-automated. Manual segmentation offers full control over
the quality of the results, but is tedious, time consuming and prone to
operator bias. Fully automated methods require no human effort, but often
deliver sub-optimal results without providing users with the means to make
corrections. Semi-automated approaches keep users in control of the results by
providing means for interaction, but the main challenge is to offer a good
trade-off between precision and required interaction. In this paper we present
a deep learning (DL) based semi-automated segmentation approach that aims to be
a "smart" interactive tool for region of interest delineation in medical
images. We demonstrate its use for segmenting multiple organs on computed
tomography (CT) of the abdomen. Our approach solves some of the most pressing
clinical challenges: (i) it requires only one to a few user clicks to deliver
excellent 2D segmentations in a fast and reliable fashion; (ii) it can
generalize to previously unseen structures and "corner cases"; (iii) it
delivers results that can be corrected quickly in a smart and intuitive way up
to an arbitrary degree of precision chosen by the user and (iv) ensures high
accuracy. We present our approach and compare it to other techniques and
previous work to show the advantages brought by our method.
