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For medical image segmentation, most fully convolutional networks (FCNs) needstrong supervision through a large sample of high-quality dense segmentations,which is taxing in terms of costs, time and logistics involved. This burden ofannotation can be alleviated by exploiting weak inexpensive annotations such asbounding boxes and anatomical landmarks. However, it is very difficult to\textit{a priori} estimate the optimal balance between the number ofannotations needed for each supervision type that leads to maximum performancewith the least annotation cost. To optimize this cost-performance trade off, wepresent a budget-based cost-minimization framework in a mixed-supervisionsetting via dense segmentations, bounding boxes, and landmarks. We propose alinear programming (LP) formulation combined with uncertainty and similaritybased ranking strategy to judiciously select samples to be annotated next foroptimal performance. In the results section, we show that our proposed methodachieves comparable performance to state-of-the-art approaches withsignificantly reduced cost of annotations.

Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks