# Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks

arXiv: https://arxiv.org/abs/2011.08761 · HTML: https://mlsanity.com/paper/2011.08761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network](https://mlsanity.com/paper/2211.06641/) (2022)
- [Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy](https://mlsanity.com/paper/2211.07088/) (2022)
- [Orientation recognition and correction of Cardiac MRI with deep neural network](https://mlsanity.com/paper/2211.11336/) (2022)
- [Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks](https://mlsanity.com/paper/2308.00615/) (2023)

## Abstract (arXiv)

In this paper, we study the problem of imaging orientation in cardiac MRI, and propose a framework to categorize the orientation for recognition and standardization via deep neural networks. The method uses a new multi-tasking strategy, where both the tasks of cardiac segmentation and orientation recognition are simultaneously achieved. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We embed the orientation recognition network in a Cardiac MRI Orientation Adjust Tool, i.e., CMRadjustNet. We implemented two versions of CMRadjustNet, including a user-interface (UI) software, and a command-line tool. The former version supports MRI image visualization, orientation prediction, adjustment, and storage operations; and the latter version enables the batch operations. The source code, neural network models and tools have been released and open via https://zmiclab.github.io/projects.html.