  Semantic segmentation of medical images is an essential first step in
computer-aided diagnosis systems for many applications. However, given many
disparate imaging modalities and inherent variations in the patient data, it is
difficult to consistently achieve high accuracy using modern deep neural
networks (DNNs). This has led researchers to propose interactive image
segmentation techniques where a medical expert can interactively correct the
output of a DNN to the desired accuracy. However, these techniques often need
separate training data with the associated human interactions, and do not
generalize to various diseases, and types of medical images. In this paper, we
suggest a novel conditional inference technique for DNNs which takes the
intervention by a medical expert as test time constraints and performs
inference conditioned upon these constraints. Our technique is generic can be
used for medical images from any modality. Unlike other methods, our approach
can correct multiple structures simultaneously and add structures missed at
initial segmentation. We report an improvement of 13.3, 12.5, 17.8, 10.2, and
12.4 times in user annotation time than full human annotation for the nucleus,
multiple cells, liver and tumor, organ, and brain segmentation respectively. We
report a time saving of 2.8, 3.0, 1.9, 4.4, and 8.6 fold compared to other
interactive segmentation techniques. Our method can be useful to clinicians for
diagnosis and post-surgical follow-up with minimal intervention from the
medical expert. The source-code and the detailed results are available here
[1].
