Shape-based interpolation method in measuring intracranial volume for pre- and post-operative decompressive craniectomy using open source software.
Abstract Introduction Intracranial volume (ICV) is an important tool in the management of patients undergoing decompressive craniectomy (DC) surgery. The aim of this study was to validate ICV measurement applying the shape-based interpolation (SBI) method using open source software on computed tomog...
| Publicado en: | Neurocirugía Vol. 30; no. 3; pp. 115 - 124 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
May2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=135928608&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 135928608 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 11301473 25ME jtl: Neurocirugía issn: 11301473 maglogo: N pubinfo: dt: May2019 vid: 30 iid: 3 pid: 467 pub: Elsevier B.V. artinfo: ui: 135928608 10.1016/j.neucir.2018.12.004 ppf: 115 ppct: 9 formats: tig: atl: Shape-based interpolation method in measuring intracranial volume for pre- and post-operative decompressive craniectomy using open source software. aug: au: Abdullah, Johari Yap Rajion, Zainul Ahmad Martin, Arvind Gerard Jaafar, Azlan Ghani, Abdul Rahman Izaini Abdullah, Jafri Malin affil: Craniofacial Medical Imaging Research Group, School of Dental Sciences, Universiti Sains Malaysia, Kelantan, Malaysia Department of Neurosurgery, Hospital Tengku Ampuan Afzan, Jalan Air Putih, Kuantan, Pahang, Malaysia Faculty of Dentistry, Universiti Sains Islam Malaysia, Kuala Lumpur, Malaysia Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia Centre for Neuroscience Services & Research, Universiti Sains Malaysia, Kelantan, Malaysia su: Decompressive craniectomy Open source software Interpolation Intraclass correlation Bland-Altman plot Sampling (Process) sug: subj: Decompressive craniectomy Open source software Interpolation Intraclass correlation Bland-Altman plot Sampling (Process) keyword: Computed tomography Craneotomía descompresiva Intracranial volume Método de interpolación basado en formas Shape-based interpolation method Software de código abierto Tomografía computarizada Volumen intracraneal ab: Abstract Introduction Intracranial volume (ICV) is an important tool in the management of patients undergoing decompressive craniectomy (DC) surgery. The aim of this study was to validate ICV measurement applying the shape-based interpolation (SBI) method using open source software on computed tomography (CT) images. Methods The pre- and post-operative CT images of 55 patients undergoing DC surgery were analyzed. The ICV was measured by segmenting every slice of the CT images, and compared with estimated ICV calculated using the 1-in-10 sampling strategy and processed using the SBI method. An independent t test was conducted to compare the ICV measurements between the two different methods. The calculation using this method was repeated three times for reliability analysis using the intraclass correlations coefficient (ICC). The Bland–Altman plot was used to measure agreement between the methods for both pre- and post-operative ICV measurements. Results The mean ICV (±SD) were 1341.1 ± 122.1 ml (manual) and 1344.11 ± 122.6 ml (SBI) for the preoperative CT data. The mean ICV (±SD) were 1396.4 ± 132.4 ml (manual) and 1400.53 ± 132.1 ml (SBI) for the post-operative CT data. No significant difference was found in ICV measurements using the manual and the SBI methods (p =.983 for pre-op, and p =.960 for post-op). The intrarater ICC showed a significant correlation; ICC = 1.00. The Bland–Altman plot showed good agreement between the manual and the SBI method. Conclusion The shape-based interpolation method with 1-in-10 sampling strategy gave comparable results in estimating ICV compared to manual segmentation. Thus, this method could be used in clinical settings for rapid, reliable and repeatable ICV estimations. Resumen Introducción El volumen intracraneal (ICV) constituye una importante herramienta para el tratamiento de los pacientes sometidos a craneotomía descompresiva (CD). El objetivo de este estudio fue validar la medición del ICV aplicando el método de interpolación basado en formas (SBI), utilizando software de código abierto en las imágenes de tomografía computarizada (TC). Métodos Se analizaron las imágenes pre- y postoperatorias de TC de 55 pacientes sometidos a CD. Se midió el ICV segmentando cada corte de las imágenes de TC, comparándose con el ICV estimado calculado utilizando la estrategia de muestreo 1 de 10, y procesándose mediante el método SBI. Se realizó una prueba t independiente para comparar las mediciones de ICV entre los 2 métodos. Se repitió 3 veces el cálculo con este método, para realizar el análisis de fiabilidad, utilizando el coeficiente de correlación intra-clase (ICC). Se utilizó el gráfico de Bland-Altman para medir el acuerdo entre ambos métodos, para las mediciones de ICV pre- y postoperatorias. Resultados El ICV medio (± DE) fue de 1.341,1 ml ± 122,1 (manual) y 1.344,11 ml ± 122,6 (SBI) para los datos de la TC preoperatoria. El ICV medio (± DE) fue de 1.396,4 ml ± 132,4 (manual) y 1.400,53 ml ± 132,1 (SBI) para los datos de la TC postoperatoria. No se encontró diferencia significativa en cuanto a las mediciones de ICV utilizando los métodos manual y SBI (p = 0,983 para preoperatoria, p = 0,960 para postoperatoria). El ICC intra-evaluador reflejó una correlación significativa; ICC = 1. El gráfico de Bland-Altman reflejó un buen acuerdo entre el método manual y el método SBI. Conclusión El método de interpolación basado en formas con estrategia de muestreo 1 de 10 proporcionó resultados comparables a la hora de calcular el ICV, en comparación con la segmentación manual. Por tanto, este método podría utilizarse en el entorno clínico para realizar estimaciones del ICV rápidas, fidedignas y repetibles. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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