Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

TLDR

Multimodal near‑infrared imaging that fuses NIR with MRI/CT/ultrasound improves optical parameter estimation, yet current workflows require multiple software packages, expertise, and time, and often produce poor mesh quality, hindering translational research. This study presents a free, integrated software package that automates DICOM stack segmentation and generates a one‑click 3‑D mesh optimized for multimodal NIR imaging. Benchmarking on breast, brain, pancreas, and small‑animal cases showed the new tool’s image‑processing time and mesh quality compared favorably against a commercial package. The new workflow reduced processing time fivefold and improved minimum mesh quality by 62 % without additional post‑processing, enabling translational multimodal NIR research for both experts and non‑experts on an open‑source platform.

Abstract

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.