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An Innovative 3D Slicer Plugin for Brain Images Annotation and Lesions Study.

Marida de Maria¹, Gianluigi Attanasio², Martina de Salazar³

¹Mediterranea University of Reggio Calabria, Italy.

Studies in Health Technology and Informatics

|May 23, 2026

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A new plugin integrates deep learning models into 3D Slicer for biomedical image analysis. This tool enhances visualization and prediction accuracy using convolutional neural networks (CNNs).

Area of Science:

Medical Imaging
Artificial Intelligence
Computer-Aided Diagnosis

Background:

Biomedical image analysis often requires specialized software.
Integrating advanced machine learning models can improve diagnostic accuracy.
Open-source platforms offer flexibility for research and development.

Purpose of the Study:

To design and validate a plugin for 3D Slicer.
To enable deep learning inference for biomedical image analysis within Slicer.
To provide a seamless workflow for image processing, prediction, and visualization.

Main Methods:

Developed a plugin integrating deep learning models into 3D Slicer.
Implemented a VGG16-based convolutional neural network (CNN).
Utilized ONNX Runtime for model inference within the Slicer framework.

Keywords:

3D SLicer Biomedical Image Analysis CNN VGG16

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Employed pre-processing techniques including format conversion, normalization, and data augmentation.

Main Results:

The plugin facilitates image loading, inference, and result visualization in 3D Slicer.
Achieved approximately 85% accuracy and an Area Under the Curve (AUC) of 0.94.
Demonstrated improved performance on high-definition medical images.

Conclusions:

The developed plugin effectively integrates deep learning into 3D Slicer for biomedical imaging.
The system offers a modular architecture for efficient image analysis workflows.
This approach shows promise for enhancing computer-aided diagnosis in medical imaging.