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Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning
F. Martínez-Martínez, J. Kybic, L. Lambert, Z. Mecková,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, randomizované kontrolované studie, práce podpořená grantem
NLK
ProQuest Central
od 2003-01-01 do 2023-12-31
Nursing & Allied Health Database (ProQuest)
od 2003-01-01 do 2023-12-31
Health & Medicine (ProQuest)
od 2003-01-01 do 2023-12-31
- MeSH
- lidé středního věku MeSH
- lidé MeSH
- metastázy nádorů MeSH
- mnohočetný myelom MeSH
- nádory kostní dřeně MeSH
- počítačová rentgenová tomografie metody MeSH
- počítačové zpracování obrazu * MeSH
- senioři MeSH
- strojové učení * MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- randomizované kontrolované studie MeSH
This paper presents a fully automated method for the identification of bone marrow infiltration in femurs in low-dose CT of patients with multiple myeloma. We automatically find the femurs and the bone marrow within them. In the next step, we create a probabilistic, spatially dependent density model of normal tissue. At test time, we detect unexpectedly high density voxels which may be related to bone marrow infiltration, as outliers to this model. Based on a set of global, aggregated features representing all detections from one femur, we classify the subjects as being either healthy or not. This method was validated on a dataset of 127 subjects with ground truth created from a consensus of two expert radiologists, obtaining an AUC of 0.996 for the task of distinguishing healthy controls and patients with bone marrow infiltration. To the best of our knowledge, no other automatic image-based method for this task has been published before.
Citace poskytuje Crossref.org
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- $a This paper presents a fully automated method for the identification of bone marrow infiltration in femurs in low-dose CT of patients with multiple myeloma. We automatically find the femurs and the bone marrow within them. In the next step, we create a probabilistic, spatially dependent density model of normal tissue. At test time, we detect unexpectedly high density voxels which may be related to bone marrow infiltration, as outliers to this model. Based on a set of global, aggregated features representing all detections from one femur, we classify the subjects as being either healthy or not. This method was validated on a dataset of 127 subjects with ground truth created from a consensus of two expert radiologists, obtaining an AUC of 0.996 for the task of distinguishing healthy controls and patients with bone marrow infiltration. To the best of our knowledge, no other automatic image-based method for this task has been published before.
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