A Segmentation Framework for Accurate Diagnosis of Amyloid Positivity without Structural Images

TL;DR

This paper introduces a deep learning framework that accurately segments brain regions and classifies amyloid positivity using only PET images, eliminating the need for structural MRI or CT scans.

Contribution

It presents a novel 3D U-Net based method for PET-only amyloid segmentation and classification, improving diagnostic efficiency without structural imaging.

Findings

High segmentation accuracy with Dice scores up to 0.88

Achieved 98% accuracy in amyloid positivity classification

Model demonstrates potential for PET-only diagnostic pipelines

Abstract

This study proposes a deep learning-based framework for automated segmentation of brain regions and classification of amyloid positivity using positron emission tomography (PET) images alone, without the need for structural MRI or CT. A 3D U-Net architecture with four layers of depth was trained and validated on a dataset of 200 F18-florbetapir amyloid-PET scans, with an 130/20/50 train/validation/test split. Segmentation performance was evaluated using Dice similarity coefficients across 30 brain regions, with scores ranging from 0.45 to 0.88, demonstrating high anatomical accuracy, particularly in subcortical structures. Quantitative fidelity of PET uptake within clinically relevant regions. Precuneus, prefrontal cortex, gyrus rectus, and lateral temporal cortex was assessed using normalized root mean square error, achieving values as low as 0.0011. Furthermore, the model achieved a classification accuracy of 0.98 for amyloid positivity based on regional uptake quantification, with an area under the ROC curve (AUC) of 0.99. These results highlight the model's potential for integration into PET only diagnostic pipelines, particularly in settings where structural imaging is not available. This approach reduces dependence on coregistration and manual delineation, enabling scalable, reliable, and reproducible analysis in clinical and research applications. Future work will focus on clinical validation and extension to diverse PET tracers including C11 PiB and other F18 labeled compounds.