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Deep Learning-based Image Segmentation on Multimodal Medical Imaging.

Zhe Guo¹, Xiang Li², Heng Huang³

¹School of Information and Electronics, Beijing Institute of Technology, China.

IEEE Transactions on Radiation and Plasma Medical Sciences

|November 1, 2021

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This study introduces a deep learning framework for analyzing multi-modal medical images, improving soft tissue sarcoma segmentation. Fusing images within the network enhances performance over single-modality analysis.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Multi-modality medical imaging is crucial for clinical practice and research.
Deep learning advancements are driving progress in medical image analysis.
Ensemble learning and multi-modal analysis offer unique value in medical applications.

Purpose of the Study:

To propose a deep learning architecture for supervised multi-modal image analysis.
To develop an image segmentation system for soft tissue sarcomas using multi-modal data.
To provide empirical guidance on optimal fusion strategies in multi-modal image analysis.

Main Methods:

Developed a deep Convolutional Neural Network (CNN) architecture for cross-modality fusion.
Implemented fusion at feature learning, classifier, and decision-making levels.

Keywords:

Computed Tomography (CT)Convolutional Neural Network Magnetic Resonance Imaging (MRI)Multi-modal Image Positron Emission Tomography (PET)

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Segmented soft tissue sarcoma lesions using Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) data.

Main Results:

Networks trained with multi-modal images demonstrated superior segmentation performance compared to single-modal training.
Intra-network fusion (e.g., at convolutional layers) yielded better tumor segmentation results than output-level fusion (e.g., voting).
The proposed system effectively contoured soft tissue sarcoma lesions.

Conclusions:

Deep learning-based multi-modal image analysis significantly enhances medical image segmentation accuracy.
Intra-network fusion strategies are more effective for tumor segmentation tasks.
This research offers valuable insights for designing and applying multi-modal image analysis systems in clinical settings.