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Samenvatting
Automated lesion segmentation is essential to provide fast, reproducible tumor load estimates. Though deep learning methods have achieved unprecedented results in this field, they are often difficult to interpret, hampering their potential integration in the clinic. An interpretable deep learning approach is proposed for segmenting melanoma lesions on whole-body fluorine-18 fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) / computed tomography (CT). This consists of an automated PET thresholding step to identify FDGavid regions, followed by a three-channel nnU-Net considering the binary mask in addition to the PET and CT images. This segmentation step differentiates healthy from malignant tissue and removes the restriction on lesion boundaries imposed by the thresholding. The proposed method, trained on 267 images and evaluated on two sets acquired at the same institute, achieved mean Dice similarity coefficients (DSC) of 0.779 and 0.638 with mean absolute volume differences of 15.2mL and 22.0 mL. The DSC proved significantly higher compared to a direct, two-channel nnU-Net considering only the PET and CT. The same was observed when retraining and testing on subsets of the public data of the autoPET challenge, containing melanoma, lung cancer and lymphoma patients. In addition, overall results proved superior to a previously proposed two-step approach, where a classification network categorized each component of increased tracer uptake as healthy or malignant. The proposed lesion segmentation method for whole-body [18F]FDG PET/CT incorporates prior thresholding information while allowing more flexibility in the lesion delineation than a pure thresholding approach and increased interpretability over a direct segmentation network.
Originele taal-2 | English |
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Titel | Proc. SPIE 12926, Medical Imaging 2024: Image Processing |
Uitgeverij | SPIE |
Aantal pagina's | 10 |
Volume | 12926 |
DOI's | |
Status | Published - 2 apr 2024 |
Evenement | SPIE Medical Imaging 2024 - Duur: 18 feb 2024 → 22 feb 2024 |
Publicatie series
Naam | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
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Volume | 12926 |
ISSN van geprinte versie | 1605-7422 |
Conference
Conference | SPIE Medical Imaging 2024 |
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Periode | 18/02/24 → 22/02/24 |
Bibliografische nota
Publisher Copyright:© 2024 SPIE.
Vingerafdruk
Duik in de onderzoeksthema's van 'An interpretable deep learning approach for lesion detection and segmentation on whole-body [18F]FDG PET/CT'. Samen vormen ze een unieke vingerafdruk.Projecten
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SRP62: SRP-Groeifinanciering: Single-domain antibody fragment (SdAb)-based TArgeted Radionuclide Therapy: STaRT programme
Keyaerts, M., D'Huyvetter, M. & Neyns, B.
1/03/19 → 30/09/24
Project: Fundamenteel