Poster + Paper
3 April 2023 State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images
Author Affiliations +
Conference Poster
Abstract
The prostate-specific membrane antigen (PSMA) is a powerful target for positron emission tomography (PET) that has opened a new era in the diagnosis and management of prostate cancer (PCa). Aiming to provide an automated diagnostic and management tool that can help detect metastatic PCa lesions in PSMA-PET images, we deployed and investigated an array of state-of-the-art deep learning-based object detection algorithms (4 categories of multi-stage, single-stage, anchor-free, and end-to-end transformer-based). The results of 17 trained networks are reported in terms of 3 metrics (precision, recall, and F1 score), showing the ability of object detection models to localize PCa metastatic lesions of different sizes and standard uptake values (SUV). Our goal is to provide a fully automated computer-aided diagnosis (CAD) tool to assist physicians in performing the diagnosis by significantly saving time and decreasing false-negative rates. A novelty of the present work is to focus on multiple rotations of maximum intensity projection (MIP) images computed on 3D volumes in the dataset, as a new investigative training framework for detection. .
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amirhosein Toosi, Sara Harsini, Shadab Ahamed, Fereshteh Yousefirizi, François Bénard, Carlos Uribe, and Arman Rahmim "State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643E (3 April 2023); https://doi.org/10.1117/12.2654527
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KEYWORDS
Object detection

Principal component analysis

Education and training

Positron emission tomography

Detection and tracking algorithms

Cancer detection

Image segmentation

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