An edge-driven 3D region-growing approach for upper airway morphology and volume evaluation in patients with Pierre Robin sequence

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Abstract

Abstract: Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction of the regions of interest (ROIs), useful to increase understanding of the PRS patient’s condition. In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733%) and dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.
Lingua originaleEnglish
pagine (da-a)232-253
Numero di pagine22
RivistaDefault journal
Volume2
Stato di pubblicazionePublished - 2015

Cita questo

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title = "An edge-driven 3D region-growing approach for upper airway morphology and volume evaluation in patients with Pierre Robin sequence",
abstract = "Abstract: Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction of the regions of interest (ROIs), useful to increase understanding of the PRS patient’s condition. In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733{\%}) and dice (94.6441{\%}) similarity indices and specificity (96.8895{\%}) and sensitivity (97.6682{\%}) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.",
keywords = "3D rendering, Pierre Robin sequence, airway model reconstruction, airways segmentation, multidetector CT, region growing",
author = "Cesare Gagliardo and Sergio Salerno and Salvatore Vitabile and Carmelo Militello",
year = "2015",
language = "English",
volume = "2",
pages = "232--253",
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TY - JOUR

T1 - An edge-driven 3D region-growing approach for upper airway morphology and volume evaluation in patients with Pierre Robin sequence

AU - Gagliardo, Cesare

AU - Salerno, Sergio

AU - Vitabile, Salvatore

AU - Militello, Carmelo

PY - 2015

Y1 - 2015

N2 - Abstract: Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction of the regions of interest (ROIs), useful to increase understanding of the PRS patient’s condition. In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733%) and dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.

AB - Abstract: Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction of the regions of interest (ROIs), useful to increase understanding of the PRS patient’s condition. In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733%) and dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to manual segmentation.

KW - 3D rendering

KW - Pierre Robin sequence

KW - airway model reconstruction

KW - airways segmentation

KW - multidetector CT

KW - region growing

UR - http://hdl.handle.net/10447/291810

M3 - Article

VL - 2

SP - 232

EP - 253

JO - Default journal

JF - Default journal

ER -