Segmentation procedures for the assessment of paranasal sinuses volumes

Cellina, Michaela; Gibelli, Daniele; Cappella, Annalisa; Toluian, Tahereh; Pittino, Carlo Valenti; Martinenghi, C; Oliva, Giancarlo

doi:10.1177/1971400920946635

Cited by 13 publications

(20 citation statements)

References 46 publications

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“…Semi-automatic segmentation techniques still require operator intervention for manual threshold selection. Additionally, the manual adjustments of segmented structures also require a considerable amount of time and may induce operator-based errors 31 . For overcoming the above-mentioned limitations and to provide a reproducible and consistent technique, the present study aimed to develop and validate a novel automated maxillary sinus segmentation methodology on CBCT images using a CNN-based model.…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images

Morgan

Gerven²,

Smolders³

et al. 2022

Sci Rep

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An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis. Hence, this study developed and validated a novel automated CNN-based methodology for the segmentation of maxillary sinus using CBCT images. A dataset of 264 sinuses were acquired from 2 CBCT devices and randomly divided into 3 subsets: training, validation, and testing. A 3D U-Net architecture CNN model was developed and compared to semi-automatic segmentation in terms of time, accuracy, and consistency. The average time was significantly reduced (p-value < 2.2e−16) by automatic segmentation (0.4 min) compared to semi-automatic segmentation (60.8 min). The model accurately identified the segmented region with a dice similarity co-efficient (DSC) of 98.4%. The inter-observer reliability for minor refinement of automatic segmentation showed an excellent DSC of 99.6%. The proposed CNN model provided a time-efficient, precise, and consistent automatic segmentation which could allow an accurate generation of 3D models for diagnosis and virtual treatment planning.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images

Morgan

Gerven²,

Smolders³

et al. 2022

Sci Rep

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“…Previous volumetric investigations were carried out by analyzing computed tomography data. 7 Today, high-resolution CT and MRI provide accessible options for assessing this complex morphometry. 30 3D reconstruction offers volumetric analysis and enables an advanced presurgical planning.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, size and volumetric measurements of the sphenoid sinus has been subject of several published studies. [6][7][8] Tomographic imaging such as computed tomography (CT) and cone beam computed tomography (CBCT) are fundamental for preoperative investigation and planning. While several investigations analyzed the sphenoid sinus anatomy by CT images, CBCT has not been fully elucidated yet according to this issue.…”

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confidence: 99%

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Anatomical and Volumetric Analysis of the Sphenoid Sinus by Semiautomatic Segmentation of Cone Beam Computed Tomography

Lentzen

Zirk

Riekert

et al. 2020

Journal of Craniofacial Surgery

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This investigation was performed to analyze and evaluate anatomical and volumetric data of the sphenoid sinus by semiautomatic segmentation of cone beam computed tomography (CBCT) datasets.This analysis was carried out on cone beam computed tomography images of 100 patients. By using the discretionary software ''ITK-Snap'', the volumetric investigation of 197 sphenoid sinuses was conducted. The statistical analyses were carried out by using the paired and independent Student t-test. In addition, the Pearsons chi-square test was performed. P values P < 0.05 were considered significant.The volumetric mean was 4.438 AE 2.434 cm 3 for the right and 4.809 AE 3.000 for the left side. Results did not indicate statistically significant differences according to the side, irrespective of gender (P > 0.05). The further investigation of differences between male and female patients did assess significant results (P < 0.05), males (10.477 AE 3.851 cm 3 ) present a larger total sinus volume than females (8.219 AE 3.574 cm 3 ). In accordance to patients' age (median 54 years), smaller volumes were related to younger patients and larger volumes to older ones. But, no statistically significant results in accordance to age could be detected (x 2 ¼ 1.258; P > 0.05).Endoscopic sinus surgery of the vulnerable and intricate anatomy of the sphenoid sinus requires an explicit knowledge and orientation about the anatomical variations such as septums and volumetric capacity. Concerning this matter, semiautomatic segmentation of CBCT images can aid the surgeon in preoperative planning. Our investigation shows that a small sinus volume, that is, affecting female patients and a complex sinus anatomy by the occurrence of a septum, especially located close to neurovascular structures could essentially aggravate endoscopic sphenoid sinus surgery. In this regard, volumetric analysis of the sphenoid sinus by CBCT images can provide presurgical information and facilitates an individualized treatment.

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“…Paranasal sinuses are complex anatomical structured consist of aired area ethmoid, frontal, maxillary and sphenoid sinuses with significant variation of shape, morphology and size [ 1 , 2 ]. Evaluation of paranasal sinus abnormalities using computed tomography (CT) radiograph can be based on segmentation of the volume of the paranasal sinus cavities, including mucosal thickening which considered as an objective diagnostic criterion for chronic sinusitis [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Volumetric growth analysis of maxillary sinus using computed tomography scan segmentation: a pilot study of Indonesian population

et al. 2021

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Segmentation procedures for the assessment of paranasal sinuses volumes

Cited by 13 publications

References 46 publications

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images

Anatomical and Volumetric Analysis of the Sphenoid Sinus by Semiautomatic Segmentation of Cone Beam Computed Tomography

Volumetric growth analysis of maxillary sinus using computed tomography scan segmentation: a pilot study of Indonesian population

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