# Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks

arXiv: https://arxiv.org/abs/2308.00615 · HTML: https://mlsanity.com/paper/2308.00615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 5 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Multivariate mixture model for myocardium segmentation combining multi-source images](https://mlsanity.com/paper/1612.08820/) (2016)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [MyoPS-Net: Myocardial Pathology Segmentation with Flexible Combination of Multi-Sequence CMR Images](https://mlsanity.com/paper/2211.03062/) (2022)
- [Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier](https://mlsanity.com/paper/1810.03143/) (2018)
- [Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks](https://mlsanity.com/paper/2011.08761/) (2020)

## Abstract (arXiv)

Orientation recognition and standardization play a crucial role in the effectiveness of medical image processing tasks. Deep learning-based methods have proven highly advantageous in orientation recognition and prediction tasks. In this paper, we address the challenge of imaging orientation in cardiac MRI and present a method that employs deep neural networks to categorize and standardize the orientation. To cater to multiple sequences and modalities of MRI, we propose a transfer learning strategy, enabling adaptation of our model from a single modality to diverse modalities. We conducted comprehensive experiments on CMR images from various modalities, including bSSFP, T2, and LGE. The validation accuracies achieved were 100.0%, 100.0%, and 99.4%, confirming the robustness and effectiveness of our model. Our source code and network models are available at https://github.com/rxzhen/MSCMR-orient