  We propose a novel shape-aware relation network for accurate and real-time
landmark detection in endoscopic submucosal dissection (ESD) surgery. This task
is of great clinical significance but extremely challenging due to bleeding,
lighting reflection, and motion blur in the complicated surgical environment.
Compared with existing solutions, which either neglect geometric relationships
among targeting objects or capture the relationships by using complicated
aggregation schemes, the proposed network is capable of achieving satisfactory
accuracy while maintaining real-time performance by taking full advantage of
the spatial relations among landmarks. We first devise an algorithm to
automatically generate relation keypoint heatmaps, which are able to
intuitively represent the prior knowledge of spatial relations among landmarks
without using any extra manual annotation efforts. We then develop two
complementary regularization schemes to progressively incorporate the prior
knowledge into the training process. While one scheme introduces pixel-level
regularization by multi-task learning, the other integrates global-level
regularization by harnessing a newly designed grouped consistency evaluator,
which adds relation constraints to the proposed network in an adversarial
manner. Both schemes are beneficial to the model in training, and can be
readily unloaded in inference to achieve real-time detection. We establish a
large in-house dataset of ESD surgery for esophageal cancer to validate the
effectiveness of our proposed method. Extensive experimental results
demonstrate that our approach outperforms state-of-the-art methods in terms of
accuracy and efficiency, achieving better detection results faster. Promising
results on two downstream applications further corroborate the great potential
of our method in ESD clinical practice.
