Tissue Individual Signatures through Machine Learning Analysis of Ultrasound Images.

Area of Science:

• Medical Imaging
• Machine Learning
• Surgical Technology

Background:

• Minimally invasive procedures benefit from advanced sensors and data science, but intraoperative accuracy and tool localization remain challenging.
• Ultrasound imaging offers real-time visualization but suffers from artifacts and poor instrument alignment.
• Current methods lack robust real-time tissue characterization during surgery.

Purpose of the Study:

• To develop a preliminary framework for classifying materials with tissue-like properties using ultrasound and machine learning.
• To assess the potential of ultrasound-based material classification for improving intraoperative navigation.
• To explore real-time tissue characterization through statistical learning on ultrasound data.

Main Methods:

• Artificial phantoms and biological tissues were imaged using ultrasound.
• Image features were extracted and preprocessed using Gabor, Tamura, and Hessian filters.
• Machine learning models (Random Forest, SVM, Discriminant Analysis) were trained and evaluated using the F1 score.

Main Results:

• The developed framework achieved up to 0.8 F1 score in classifying materials with tissue-like properties.
• Statistical learning combined with ultrasound imaging demonstrated potential for differentiating materials.
• Preliminary results show promise for enhanced intraoperative navigation and tissue characterization.

Conclusions:

• Ultrasound imaging coupled with statistical learning can differentiate materials with tissue-relevant properties.
• This approach holds potential for improving intraoperative navigation and real-time tissue identification.
• Future research will focus on expanding datasets and clinical integration.