Multispectral neurosurgery image analysis: preliminary segmentation network evaluated on 47 patient cohort

Zepeng Hu; Giulio Anichini; Kevin O'Neill; Daniel Elson

doi:10.1117/12.3017483

19 June 2024 Multispectral neurosurgery image analysis: preliminary segmentation network evaluated on 47 patient cohort

Zepeng Hu, Giulio Anichini, Kevin O'Neill, Daniel Elson

Proceedings Volume PC13010, Tissue Optics and Photonics III; PC130100A (2024) https://doi.org/10.1117/12.3017483
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

This study analyses intraoperative multispectral images taken from 47 brain tumour surgeries to investigate the diagnostic and surgical guidance potential of MSI. The research enrolled patients with various tumour types and introduces a hybrid model, uniting a transformer-coupled convolutional neural network (CNN), tailored for multispectral brain image segmentation. Leveraging MSI, the model was preliminarily assessed on ten meningioma and thirty-three glioma cases, each categorized into seven distinct classes. The model demonstrated a promising overall accuracies of 88.14% for meningioma and 85.64% for glioma. These initial results highlight the potential of the proposed hybrid architecture in multispectral brain image segmentation, laying the foundation for future research to optimize the model's performance with a larger patient cohort.

Conference Presentation

Citation Download Citation

Zepeng Hu, Giulio Anichini, Kevin O'Neill, and Daniel Elson "Multispectral neurosurgery image analysis: preliminary segmentation network evaluated on 47 patient cohort", Proc. SPIE PC13010, Tissue Optics and Photonics III, PC130100A (19 June 2024); https://doi.org/10.1117/12.3017483

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