  Accurate segmentation of critical anatomical structures is at the core of
medical image analysis. The main bottleneck lies in gathering the requisite
expert-labeled image annotations in a scalable manner. Methods that permit to
produce accurate anatomical structure segmentation without using a large amount
of fully annotated training images are highly desirable. In this work, we
propose a novel contribution of Contour Transformer Network (CTN), a one-shot
anatomy segmentor including a naturally built-in human-in-the-loop mechanism.
Segmentation is formulated by learning a contour evolution behavior process
based on graph convolutional networks (GCNs). Training of our CTN model
requires only one labeled image exemplar and leverages additional unlabeled
data through newly introduced loss functions that measure the global shape and
appearance consistency of contours. We demonstrate that our one-shot learning
method significantly outperforms non-learning-based methods and performs
competitively to the state-of-the-art fully supervised deep learning
approaches. With minimal human-in-the-loop editing feedback, the segmentation
performance can be further improved and tailored towards the observer desired
outcomes. This can facilitate the clinician designed imaging-based biomarker
assessments (to support personalized quantitative clinical diagnosis) and
outperforms fully supervised baselines.
