Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00239448" target="_blank" >RIV/68407700:21230/16:00239448 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/16:10325486

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.compbiomed.2016.02.001" target="_blank" >http://dx.doi.org/10.1016/j.compbiomed.2016.02.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2016.02.001" target="_blank" >10.1016/j.compbiomed.2016.02.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning

Popis výsledku v původním jazyce

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 healt hy 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.

Název v anglickém jazyce

Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning

Popis výsledku anglicky

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 healt hy 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

—

Svazek periodika

71

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

57-66

Kód UT WoS článku

000373750200006

EID výsledku v databázi Scopus

2-s2.0-84961885056