Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images

Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. This work presents a fully-automated segmentation technique using a multi-center dataset of diffuse large B-cell lymphoma (DLBCL) with heterogeneous characteristics. We utilized a dataset...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Yousefirizi, Fereshteh, Dubljevic, Natalia, Ahamed, Shadab, Bloise, Ingrid, Gowdy, Claire, Joo, Hyun O, Farag, Youssef, Rodrigue de Schaetzen, Martineau, Patrick, Wilson, Don, Uribe, Carlos F, Rahmim, Arman
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Datasets
Image segmentation
Lesions
Lymphoma
Metric space
Modules
Neural networks
Organs
Positron emission
Three dimensional models
Tomography
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Summary:Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. This work presents a fully-automated segmentation technique using a multi-center dataset of diffuse large B-cell lymphoma (DLBCL) with heterogeneous characteristics. We utilized a dataset of [18F]FDG-PET scans (n=194) from two different imaging centers, including cases with primary mediastinal large B-cell lymphoma (PMBCL) (n=104). Automated brain and bladder removal approaches were utilized as preprocessing steps to tackle false positives caused by normal hypermetabolic uptake in these organs. Our segmentation model is a convolutional neural network (CNN) based on a 3D U-Net architecture that includes squeeze and excitation (SE) modules. Hybrid distribution, region, and boundary-based losses (Unified Focal and Mumford-Shah (MS)) were utilized that showed the best performance compared to other combinations (p
ISSN:2331-8422