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Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical
Michael Jermyn1, Hamid Ghadyani, Michael A Mastanduno
1Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire 03755, USA. michael.jermyn@dartmouth.edu
Journal of Biomedical Optics
|August 15, 2013
Summary
This study introduces a new, free software package for multimodal near-infrared (NIR) imaging that automates image segmentation and 3D mesh generation. This streamlines data processing, significantly improving mesh quality and reducing time for translational research.
Area of Science:
- Biomedical Optics
- Medical Imaging
- Computational Imaging
Background:
- Multimodal imaging combining near-infrared (NIR) with conventional modalities enhances optical parameter estimation.
- Current methods for integrating anatomical data (MRI, CT, ultrasound) into NIR imaging are complex, time-consuming, and yield poor mesh quality.
- These limitations hinder the translation of multimodal NIR imaging into clinical practice.
Purpose of the Study:
- To develop an automated, streamlined software solution for multimodal NIR imaging data processing.
- To improve the efficiency and quality of anatomical template integration and 3D mesh generation for optical image reconstruction.
- To facilitate translational research in multimodal NIR imaging for both expert and non-expert users.
Main Methods:
- Introduction of automated Digital Imaging and Communications in Medicine (DICOM) image stack segmentation.
- Development of a one-click 3D mesh generator specifically optimized for multimodal NIR imaging.
- Integration of these tools into a single, free, open-source software package with a streamlined workflow.
- Benchmarking image processing time and mesh quality against a commercial package for breast, brain, pancreas, and small animal imaging.
Main Results:
- A fivefold decrease in image processing time was achieved compared to a commercial package.
- A 62% improvement in minimum mesh quality was observed without additional postprocessing.
- The software package demonstrated effective performance across diverse anatomical regions and scales.
Conclusions:
- The developed open-source software significantly overcomes existing roadblocks in multimodal NIR imaging data processing.
- Automated segmentation and mesh generation enhance efficiency and quality, accelerating translational research.
- This tool democratizes multimodal NIR imaging, making it more accessible for broader research applications.
