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Image segmentation plays an essential role in medicine for both diagnosticand interventional tasks. Segmentation approaches are either manual,semi-automated or fully-automated. Manual segmentation offers full control overthe quality of the results, but is tedious, time consuming and prone tooperator bias. Fully automated methods require no human effort, but oftendeliver sub-optimal results without providing users with the means to makecorrections. Semi-automated approaches keep users in control of the results byproviding means for interaction, but the main challenge is to offer a goodtrade-off between precision and required interaction. In this paper we presenta deep learning (DL) based semi-automated segmentation approach that aims to bea "smart" interactive tool for region of interest delineation in medicalimages. We demonstrate its use for segmenting multiple organs on computedtomography (CT) of the abdomen. Our approach solves some of the most pressingclinical challenges: (i) it requires only one to a few user clicks to deliverexcellent 2D segmentations in a fast and reliable fashion; (ii) it cangeneralize to previously unseen structures and "corner cases"; (iii) itdelivers results that can be corrected quickly in a smart and intuitive way upto an arbitrary degree of precision chosen by the user and (iv) ensures highaccuracy. We present our approach and compare it to other techniques andprevious work to show the advantages brought by our method.

Interactive segmentation of medical images through fully convolutional neural networks