Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

Jiacheng Wang, Yueming Jin, Shuntian Cai, Hongzhi Xu, Pheng Ann Heng, Jing Qin, Liansheng Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number102291
JournalMedical Image Analysis
Volume75
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Endoscopic submucosal dissection
  • Landmark detection
  • Real-time detection
  • Shape-aware relation network

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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