Recognition of Cardiac MRI Orientation via Deep Neural Networks and a Method to Improve Prediction Accuracy

TL;DR

This paper presents a deep learning approach to automatically recognize cardiac MRI orientation, employing transfer learning across modalities and a voting-based prediction method to enhance accuracy, reducing manual reorientation efforts.

Contribution

It introduces a transfer learning strategy for multi-modality MRI orientation recognition and a voting-based prediction method to improve accuracy.

Findings

Deep neural networks effectively recognize cardiac MRI orientation.

Transfer learning adapts models across multiple MRI modalities.

Voting method enhances prediction accuracy.

Abstract

In most medical image processing tasks, the orientation of an image would affect computing result. However, manually reorienting images wastes time and effort. In this paper, we study the problem of recognizing orientation in cardiac MRI and using deep neural network to solve this problem. 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 also propose a prediction method that uses voting. The results shows that deep neural network is an effective way in recognition of cardiac MRI orientation and the voting prediction method could improve accuracy.