Researchers from the Department of Computer Science and Engineering at Amrita Vishwa Vidyapeetham, including Arunkumar V, Firos V M, Senthilkumar S, and Gangadharan G R, have developed a novel approach to improve medical imaging analysis, which could have significant implications for the energy industry’s use of similar technologies.
The team introduced an Adaptive Quaternion Cross-Fusion Network (A-QCF-Net) designed to enhance the accuracy of liver tumor segmentation using multimodal medical imaging. The challenge they addressed is the scarcity of large datasets where different imaging modalities, such as CT and MRI, are paired and spatially aligned. Their solution leverages the efficiency and expressive power of Quaternion Neural Networks to create a shared feature space, enabling the model to learn from unpaired CT and MRI datasets.
At the heart of A-QCF-Net is the Adaptive Quaternion Cross-Fusion (A-QCF) block, an attention module that facilitates bidirectional knowledge transfer between the two imaging streams. This dynamic modulation of information flow allows the network to exchange modality-specific expertise, such as the sharp anatomical boundaries from CT scans and the subtle soft tissue contrast from MRI images. This mutual exchange enriches and regularizes the feature representations of both streams, leading to more accurate tumor segmentation.
The researchers validated their framework by training a single model on the unpaired LiTS (CT) and ATLAS (MRI) datasets. The jointly trained model achieved Tumor Dice scores of 76.7% on CT and 78.3% on MRI, significantly outperforming the strong unimodal nnU-Net baseline by margins of 5.4% and 4.7%, respectively. Additionally, explainability analysis using Grad-CAM and Grad-CAM++ confirmed that the model correctly focuses on relevant pathological structures, ensuring clinically meaningful representations.
For the energy industry, this research highlights the potential of advanced neural network architectures to integrate and analyze diverse data sources more effectively. Similar approaches could be applied to enhance the interpretation of complex datasets in energy exploration, reservoir monitoring, and infrastructure inspection, leading to improved decision-making and operational efficiency. The research was published in the journal Medical Image Analysis.
This article is based on research available at arXiv.