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Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer by Kevin Ma; Stephanie A. Harmon; Ivan S. Klyuzhin; Arman Rahmim; Baris Turkbey is a Medicine article available to read on EtoBox.

What is Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer about?

Artificial intelligence (AI) algorithms, particularly those based on convolutional neural networks, have shown promise for organ and lesion segmentation, patient-level and region-level classification of pathological findings, and outcome predictions in cancer imaging. Advantages of automated, AIbased image segmentation, and classification include higher annotation and processing speed than manual annotation, repeatable segmentation results, and the ability to identify and capture complex patterns or features in the images. In PET/CT imaging of prostate cancer, however, unique challenges exist for AI-based image analysis. Manual segmentations of prostate based on CT data can often include bladder tissues, and coupled with higher radiation uptake of bladder, can result in inaccurate segmentations. Additional challenges include (a) small lesion sizes that and the resulting strong voxel-wise class imbalance, (b) wide variation in the number of lesions per patient, (c) physiologic tracer accumulation in the organs such as ureters, ganglia, and bladder, (d)

Who reads Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer?

Author

Kevin Ma; Stephanie A. Harmon; Ivan S. Klyuzhin; Arman Rahmim; Baris Turkbey

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Medicine (Health Sciences)