Data/hora: 09/06/2024 12:48:51

Provedor de dados: 	56	
País: 	Brazil	
Título: 	Manual and semiautomatic segmentation of bone sarcomas on MRI have high similarity	
Autores: 	Dionísio,F.C.F.; Oliveira,L.S.; Hernandes,M.A.; Engel,E.E.; Rangayyan,R.M.; Azevedo-Marques,P.M.; Nogueira-Barbosa,M.H.	
Data: 	2020-01-01	
Ano: 	2020	
Palavras-chave: 	Ewing sarcoma; Osteosarcoma; Manual segmentation; Semiautomatic segmentation.	
Resumo: 	The aims of this study were to evaluate the intra- and interobserver reproducibility of manual segmentation of bone sarcomas in magnetic resonance imaging (MRI) studies and to compare manual and semiautomatic segmentation methods. This retrospective study included twelve osteosarcoma and eight Ewing sarcoma MRI studies performed prior to any therapeutic intervention. All cases were histopathologically confirmed. Three radiologists used 3D-Slicer software to perform manual segmentation of bone sarcomas in a blinded and independent manner. One radiologist segmented manually and also performed semiautomatic segmentation with the GrowCut tool. Segmentation exercises were timed for comparison. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate similarity between the segmentation results and further statistical analyses were performed to compare DSC, HD, and volumetric results. Manual segmentation was reproducible with intraobserver DSC varying from 0.83 to 0.97 and HD from 3.37 to 28.73 mm. Interobserver DSC of manual segmentation showed variation from 0.73 to 0.97 and HD from 3.93 to 33.40 mm. Semiautomatic segmentation compared to manual segmentation resulted in DSCs of 0.71−0.96 and HDs of 5.38−31.54 mm. Semiautomatic segmentation required significantly less time compared to manual segmentation (P value ≤0.05). Among all situations compared, tumor volumetry did not show significant statistical differences (P value &gt;0.05). We found excellent intra- and interobserver agreement for manual segmentation of osteosarcoma and Ewing sarcoma. There was high similarity between manual and semiautomatic segmentation, with a significant reduction of segmentation time using the semiautomatic method.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2020000200605	
Editor: 	Associação Brasileira de Divulgação Científica	
Relação: 	10.1590/1414-431x20198962	
Formato: 	text/html	
Fonte: 	Brazilian Journal of Medical and Biological Research  v.53 n.2 2020	
Fonte: 	info:eu-repo/semantics/openAccess