Comparison of Segmentation Methods in Analysis of MR and CT Images of Pediatric Spine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU144215" target="_blank" >RIV/00216305:26220/21:PU144215 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/PIERS53385.2021.9694940" target="_blank" >http://dx.doi.org/10.1109/PIERS53385.2021.9694940</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PIERS53385.2021.9694940" target="_blank" >10.1109/PIERS53385.2021.9694940</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Segmentation Methods in Analysis of MR and CT Images of Pediatric Spine

Popis výsledku v původním jazyce

Scoliosis is the most common spinal deformity in children. Only early treatment during spinal growth can significantly reduce the associated problems caused by the deformity in adults. The aim of this study is to use a spine model to numerically simulate the changes in spinal stresses during correction of congenital deformity by vertebral osteotomy. In the first stage, CT imaging was used as a reference to obtain correctly segmented vertebral groups due to the low quality of MRI image data. Registration techniques were optimized to process all MRI and CT image sequences. An SVM classifier was used with Dice coefficients of 0.98 for CT and 0.95, 0.97, 0.91 and 0.92 for T1 hard, T2 hard, T1 soft and T2 soft, respectively. In the next phase of the project, deep learning algorithms were used to obtain MRI segmentation. Two different segmentation algorithms were proposed using the U-Net network. Standard and patchwise approach with rotational averaging for both CT and MRI dataset. The standard segmentation produced more accurate results with a Dice coefficient of 0.96 for the CT dataset and 0.94 for the MRI dataset. The patchwise method provided slightly better results when processing the actual dataset containing the new data acquired by our MRI scanner. With the smaller MRI dataset, we achieved comparable Dice coefficients in both datasets. The presented results suggest the possibility of using CT and even MR imaging exclusively for spine segmentation if visualization of surrounding tissues and automatic 3D spine modeling is desired.

Název v anglickém jazyce

Comparison of Segmentation Methods in Analysis of MR and CT Images of Pediatric Spine

Popis výsledku anglicky

Scoliosis is the most common spinal deformity in children. Only early treatment during spinal growth can significantly reduce the associated problems caused by the deformity in adults. The aim of this study is to use a spine model to numerically simulate the changes in spinal stresses during correction of congenital deformity by vertebral osteotomy. In the first stage, CT imaging was used as a reference to obtain correctly segmented vertebral groups due to the low quality of MRI image data. Registration techniques were optimized to process all MRI and CT image sequences. An SVM classifier was used with Dice coefficients of 0.98 for CT and 0.95, 0.97, 0.91 and 0.92 for T1 hard, T2 hard, T1 soft and T2 soft, respectively. In the next phase of the project, deep learning algorithms were used to obtain MRI segmentation. Two different segmentation algorithms were proposed using the U-Net network. Standard and patchwise approach with rotational averaging for both CT and MRI dataset. The standard segmentation produced more accurate results with a Dice coefficient of 0.96 for the CT dataset and 0.94 for the MRI dataset. The patchwise method provided slightly better results when processing the actual dataset containing the new data acquired by our MRI scanner. With the smaller MRI dataset, we achieved comparable Dice coefficients in both datasets. The presented results suggest the possibility of using CT and even MR imaging exclusively for spine segmentation if visualization of surrounding tissues and automatic 3D spine modeling is desired.

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

2021 Photonics & Electromagnetics Research Symposium (PIERS)

ISBN

978-1-7281-7247-7

ISSN

1559-9450

e-ISSN

—

Počet stran výsledku

6

Strana od-do

449-454

Název nakladatele

Neuveden

Místo vydání

neuveden

Místo konání akce

Hangzhou, China

Datum konání akce

21. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000795902300070