# Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

arXiv: https://arxiv.org/abs/2309.12805 · HTML: https://mlsanity.com/paper/2309.12805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Bottom-up Object Detection by Grouping Extreme and Center Points](https://mlsanity.com/paper/1901.08043/) (2019)
- [Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction](https://mlsanity.com/paper/1907.02757/) (2019)
- [Flowing ConvNets for Human Pose Estimation in Videos](https://mlsanity.com/paper/1506.02897/) (2015)
- [Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1806.03228/) (2018)
- [Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images](https://mlsanity.com/paper/1907.09983/) (2019)
- [Cardiac MRI Segmentation with Strong Anatomical Guarantees](https://mlsanity.com/paper/1907.02865/) (2019)
- [Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views](https://mlsanity.com/paper/2109.11715/) (2021)
- [A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2205.10572/) (2022)

## Built on by (most cited first, 1 of 1)

- [Automated Interpretable 2D Video Extraction from 3D Echocardiography](https://mlsanity.com/paper/2511.15946/) (2025)

## Abstract (arXiv)

Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: 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, annotation-free system for automatic CMR view planning. Methods: The system mines the spatial relationship, more specifically, locates the intersecting lines, between the target planes and source views, and trains deep networks to regress heatmaps defined by distances from the intersecting lines. The intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. As the spatial relationship is self-contained in properly stored data, the need for additional manual annotation is eliminated. In addition, the interplay of multiple target planes predicted in a source view is utilized in a stacked hourglass architecture to gradually improve the regression. Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. Results: The experiments include 181 CMR exams. Our system yields the mean angular difference and point-to-plane distance of 5.68 degrees and 3.12 mm, respectively. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout.