Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views
Dong Wei, Kai Ma, and Yefeng Zheng

TL;DR
This paper introduces an annotation-free, deep learning-based system for automatic cardiac MRI view planning that leverages spatial relationships between views, achieving high accuracy without manual annotations or additional imaging.
Contribution
It proposes a novel self-supervised approach that exploits intersecting lines in stored data for view planning, eliminating manual annotation and improving accuracy over existing methods.
Findings
Achieves mean angle difference of 5.98 degrees
Point-to-plane distance of 3.48 mm
Outperforms atlas-based and deep learning approaches
Abstract
View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible and annotation-free system for automatic CMR view planning. The system mines the spatial relationship -- more specifically, locates and exploits the intersecting lines -- between the source and target views, and trains deep networks to regress heatmaps defined by these intersecting lines. As the spatial relationship is self-contained in properly stored data, e.g., in the DICOM format, the need for manual annotation is eliminated. Then, a multi-view planning strategy is proposed to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Robotics and Sensor-Based Localization
