A deep learning aided bone marrow segmentation of quantitative fat MRI for myelofibrosis patients

A deep learning aided bone marrow segmentation of quantitative fat MRI for myelofibrosis patients

PurposeTo automate bone marrow segmentation within pelvic bones in quantitative fat MRI of myelofibrosis (MF) patients using deep-learning (DL) U-Net models.MethodsAutomated segmentation of bone marrow (BM) was evaluated for four U-Net models: 2D U-Net, 2D attention U-Net (2D A-U-Net), 3D U-Net and...

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Bibliographic Details
Main Authors: Humera Tariq, Lubomir Hadjiiski, Dariya Malyarenko, Moshe Talpaz, Kristen Pettit, Gary D. Luker, Brian D. Ross, Thomas L. Chenevert
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Oncology
Subjects:
myelofibrosis
proton density fat fraction (PDFF)
pelvic MRI
in-phase (IP)
proximal femur
posterior ilium
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1498832/full
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https://www.frontiersin.org/articles/10.3389/fonc.2025.1498832/full

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