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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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DualSight: multi-stage instance segmentation framework for improved precision.

Stephen Price¹, Kiran Judd², Kyle Tsaknopoulos²

¹Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, 01609, USA. sprice@wpi.edu.

Scientific Reports

|July 28, 2025

Summary

This summary is machine-generated.

DualSight enhances metallic powder segmentation for cold spray additive manufacturing. This computer vision framework improves powder morphology analysis accuracy without needing more data or retraining.

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Area of Science:

Materials Science
Additive Manufacturing
Computer Vision

Background:

Powder morphology significantly impacts cold spray additive manufacturing quality, affecting flowability, deposition, and porosity.
Manual image analysis of powder morphology is time-consuming and labor-intensive.
Automated computer vision methods for powder analysis are limited by training data quality.

Purpose of the Study:

To present DualSight, a novel multi-stage computer vision framework.
To improve metallic powder segmentation quality for morphology analysis.
To enable more accurate powder characterization without additional data or model training.

Main Methods:

Development of a multi-stage computer vision framework named DualSight.
Application of the framework to segment metallic powders from scanning electron microscope images.
Evaluation of segmentation quality improvements without new data or retraining.

Main Results:

DualSight demonstrates improved metallic powder segmentation quality.
The framework enhances the accuracy of powder morphology extraction.
Accurate morphology data facilitates better-informed manufacturing decisions.

Conclusions:

DualSight offers a data-efficient approach to improve powder morphology analysis in additive manufacturing.
Enhanced segmentation accuracy leads to better understanding and control of cold spray processes.
This framework supports more reliable and efficient additive manufacturing of metallic materials.