Automatic bone marrow segmentation in whole-body magnetic resonance imaging: towards comprehensive, objective MRI-phenotypic bone marrow characterization in multiple myeloma

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU142139" target="_blank" >RIV/00216305:26220/21:PU142139 - isvavai.cz</a>

Výsledek na webu

<a href="https://pdf.sciencedirectassets.com/280646/1-s2.0-S2152265021X00122/1-s2.0-S2152265021021467/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjELr%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQC3revvEAOxPaAgGIvWSUJ47sMXijRUy%2BjIFu38YqfQhQIgEsheMKEET" target="_blank" >https://pdf.sciencedirectassets.com/280646/1-s2.0-S2152265021X00122/1-s2.0-S2152265021021467/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjELr%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQC3revvEAOxPaAgGIvWSUJ47sMXijRUy%2BjIFu38YqfQhQIgEsheMKEET</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic bone marrow segmentation in whole-body magnetic resonance imaging: towards comprehensive, objective MRI-phenotypic bone marrow characterization in multiple myeloma

Popis výsledku v původním jazyce

Background Whole-body magnetic resonance imaging (wb-MRI) is an important diagnostic tool for staging, risk assessment and response evaluation in myeloma. Wb-MRIs contain approximately 110 million voxels per sequence, and only a limited amount of this information can be processed and reported by radiologists to date. Deep learning has brought striking advances in biomedical image segmentation in recent years. The goal of this work was to establish an automatic whole-body bone marrow (BM) segmentation algorithm for T1-weighted MRI sequence, and to use these segmentations for comprehensive MRI-phenotypic characterization of the BM by subsequent radiomics analysis, bone by bone. Methods For 66 patients with smoldering multiple myeloma (SMM), BM was manually segmented on T1-w images. Thirty different BM compartments were individually labelled: right and left humerus, second to seventh vertebral bodies of the cervical spine (C2-C7), all vertebral bodies of the thoracic (T1-T12) and lumbar (L1-L5) spine, sa

Název v anglickém jazyce

Automatic bone marrow segmentation in whole-body magnetic resonance imaging: towards comprehensive, objective MRI-phenotypic bone marrow characterization in multiple myeloma

Popis výsledku anglicky

Background Whole-body magnetic resonance imaging (wb-MRI) is an important diagnostic tool for staging, risk assessment and response evaluation in myeloma. Wb-MRIs contain approximately 110 million voxels per sequence, and only a limited amount of this information can be processed and reported by radiologists to date. Deep learning has brought striking advances in biomedical image segmentation in recent years. The goal of this work was to establish an automatic whole-body bone marrow (BM) segmentation algorithm for T1-weighted MRI sequence, and to use these segmentations for comprehensive MRI-phenotypic characterization of the BM by subsequent radiomics analysis, bone by bone. Methods For 66 patients with smoldering multiple myeloma (SMM), BM was manually segmented on T1-w images. Thirty different BM compartments were individually labelled: right and left humerus, second to seventh vertebral bodies of the cervical spine (C2-C7), all vertebral bodies of the thoracic (T1-T12) and lumbar (L1-L5) spine, sa

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů