Pediatric Spine Segmentation and Modeling Using Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134032" target="_blank" >RIV/00216305:26220/19:PU134032 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/65269705:_____/19:00072859

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/8970894" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8970894</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970894" target="_blank" >10.1109/ICUMT48472.2019.8970894</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pediatric Spine Segmentation and Modeling Using Machine Learning

Popis výsledku v původním jazyce

Scoliosis embodies the most frequent threedimensional spinal deformity in children. Only timely treatment during the growth of the spine may significantly reduce related health problems inflicted by the deformity on adults. The results obtained via conservative therapy are problematic, and a certain degree of curvature already requires surgical treatment that at the time of writing consists of repeated spinal surgeries posing a high risk of complications. The aim is to use a spine model for computer based simulation of changes in the stress on the spine during idiopathic and syndromic deformity correction via vertebral osteotomy. A machine-learning toolbox for 3D Slicer has been developed. The toolbox has a form of an application extension. Preprocessing of the data, training and usage of the classifier is possible through a simple and modern graphical user interface. The extension is capable of performing a variety of helpful tasks such as an analysis of the impact of the size of the training vector and feature selection on classifier precision. The results suggest that the training vector size can be minimized for all of the tested classifiers. Furthermore, the random forest classifier's performance seems to be resistant to training parameter changes. Support vector machine is sensitive to training parameter changes with optimal values concentrated in a narrow feature space.

Název v anglickém jazyce

Pediatric Spine Segmentation and Modeling Using Machine Learning

Popis výsledku anglicky

Scoliosis embodies the most frequent threedimensional spinal deformity in children. Only timely treatment during the growth of the spine may significantly reduce related health problems inflicted by the deformity on adults. The results obtained via conservative therapy are problematic, and a certain degree of curvature already requires surgical treatment that at the time of writing consists of repeated spinal surgeries posing a high risk of complications. The aim is to use a spine model for computer based simulation of changes in the stress on the spine during idiopathic and syndromic deformity correction via vertebral osteotomy. A machine-learning toolbox for 3D Slicer has been developed. The toolbox has a form of an application extension. Preprocessing of the data, training and usage of the classifier is possible through a simple and modern graphical user interface. The extension is capable of performing a variety of helpful tasks such as an analysis of the impact of the size of the training vector and feature selection on classifier precision. The results suggest that the training vector size can be minimized for all of the tested classifiers. Furthermore, the random forest classifier's performance seems to be resistant to training parameter changes. Support vector machine is sensitive to training parameter changes with optimal values concentrated in a narrow feature space.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/NV18-08-00459" target="_blank" >NV18-08-00459: Prostorová analýza silového zatížení deformované rostoucí páteře a využití modelování korekčních sil k minimalizaci rozsahu operace skoliozy.</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

978-1-7281-5763-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

neuveden

Místo konání akce

Dublin

Datum konání akce

28. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000540651700045