Abstract
Background
The paranasal sinuses are complex anatomical structures, characterised by highly variable shape, morphology and size. With the introduction of multidetector scanners and the development of many post-processing possibilities, computed tomography became the gold standard technique to image the paranasal sinuses. Segmentation allows the extraction of metrical and shape data of these anatomical components that can be applied for diagnostic, education, surgical planning and simulation, and to plan minimally invasive interventions in otorhinolaryngology and neurosurgery.
Discussion
Our aim was to provide a review of the existing literature on segmentation, its types and application, and the data obtained from this procedure. The literature search was conducted on PubMed (including Medline), ScienceDirect and Google Scholar databases, using the keywords as follows: ‘paranasal sinuses’, ‘frontal sinus’, ‘maxillary sinus’, ‘sphenoid sinus’, ‘ethmoid sinus’, in all possible combinations with the keywords ‘segmentation’ and ‘volumetric analysis’. Inclusion criteria were: articles written in English, on living human subjects, on the adult population and focused on paranasal sinuses analysis.
Conclusion
This article provides an overview of the types and main application of segmentation procedures on paranasal sinuses, and the results provided by the studies on this topic.
Keywords: Paranasal sinuses, segmentation, 3D, volumes, computer application
Introduction
The paranasal sinuses are complex anatomical structures with significant interindividual variations.
The morphology and size of paranasal sinuses were traditionally assessed by injecting different materials into cadavers and executing plain radiography. The development of multidetector computed tomography (CT) has dramatically changed the approach to sinonasal anatomy and pathology, with a marked implementation of anatomical details.
Knowledge of anatomical variants of the paranasal sinuses and potential effects of their variable size is crucial for surgeons and radiologists to understand their pathology and to plan the most appropriate treatment.
Three-dimensional (3D) post-processing procedures and volume assessment through segmentation are applied for diagnostic, surgical planning and simulation, radiant treatment and to guide minimally invasive interventions,1 particularly in ear, nose and throat (ENT) surgery for robot-assisted surgery.2
Our aim was to outline the applications of segmentation of paranasal sinuses and the data obtained with this procedure through a review of the existing literature.
Examples of segmentation procedures are provided through images belonging to a study performed in our institution, with institutional review board (IRB) approval and patient consent provided.
Materials and methods
The search was conducted by one radiologist and two radiology residents on PubMed (including Medline), ScienceDirect and Google Scholar databases, using the keywords as follows: ‘paranasal sinuses’, ‘frontal sinus’, ‘maxillary sinus’, ‘sphenoid sinus’, ‘ethmoid sinus’, in all possible combinations with the keywords ‘segmentation’ and ‘volumetric analysis’.
We considered as eligibility criteria: articles written in English, articles on living human subjects and the adult population and articles focused on paranasal sinuses analysis.
The three operators independently checked the titles and abstracts of the articles found according to the eligibility criteria. Agreement was reached by consensus.
For each article, the following data were collected: the imaging techniques used for the assessment, the number of patients, the population type, the paranasal sinus investigated, the software used for the segmentation procedure, the segmentation type and the evidence obtained from the studies. One radiologist and one resident performed the data extraction and the other two reviewers evaluated the accuracy of the extracted data.
Results
A total of 39 studies were included in our review.
The selection procedure is shown in Figure 1. We collected data regarding the study population, the segmentation techniques and the main results of the studies.
Figure 1.
Flow chart showing the selection of the articles.
Segmentation procedures
Manual, semiautomatic and automatic procedures are available for paranasal sinus segmentation.
Manual segmentation requires significant experience and time; therefore, it is not applicable in everyday practice. Tingelhoff et al. assessed the accuracy of manual segmentation and its potential usefulness for robot-assisted surgery, in a study including 21 readers (10 ENT surgeons, 10 medical students and one engineer).1 The study evidenced high inter and intraindividual variances, and a mean segmentation time of 75.9 minutes, concluding that manual segmentation was time-consuming and did not provide reliable results.
Semiautomatic techniques are generally based on 3D intensity region growing or watershed transformation. The first type includes neighbouring voxels according to a pre-set intensity threshold, whereas the second type divides regions that result differently according to a specific criterion, such as the grey level.3 It requires the manual selection of a volume of interest; then, for the region growing approach, the manual position of a seed.
Shi et al. used cone beam computed tomography (CBCT) datasets of three patients to elaborate a semiautomatic segmentation algorithm for 3D volumes of maxillary sinuses: the maxillary sinus segmentation was realised by propagation from one start point to the whole sinus.4
Bui et al. used CBCT to develop a multi-step level coarse to fine active contour model segmentation to create a 3D model of the paranasal sinuses.5
Semiautomatic segmentation of intracranial structures with ITK-SNAP software has been validated by neuroimaging studies,6,7 and has proved to have excellent reproducibility.8
A fully automatic segmentation, consisting of automatic contour initialisation and active contour segmentation, has also been proposed for sinonasal structures;9 when compared with manual segmentation performed by two experienced operators, it showed precision and an F-score greater than 80%.
Sphenoid sinus segmentation
Sphenoid sinuses (SSs) are highly variable for shape and pneumatisation, with a deep location within the body of the sphenoid bone and strict relationship with intracranial structures.10,11 The development of neurosurgical interventions with a transsphenoidal approach makes the SS a way of access to the anterior skull base.10,11
The high rate of protrusion of neurovascular structures into the sphenoid cavities highlights the importance of precise anatomical knowledge in surgical and radiological practice,8,12 as variants of the SS can result in surgical complications. The pneumatisation, type and volume assessment can determine the accessibility to intracranial structures and help predict the risk of complications.13,14 Previous studies stated the need for a paranasal sinuses CT to assess the anatomy and the volumes of the SS for surgical planning.15
Nejaim et al., segmenting the SSs on 172 CBCTs, observed no influence of skeletal classes (class I, II, III), facial types (brachycephalic, mesocephalic, dolichocephalic), sex and intrasinus septa on sinuses volumes, and no significant difference between the volumes of the right and left SS;16 the last evidence was also found by Cohen et al.17 Oliveira et al., instead, found a significant difference between the right and the left side, with the greatest volume of the right post-sellar sinus, when compared with pre-sellar and sellar types.8
The influence of age was suggested, with the demonstration of significantly higher volumes in young patients.17
The correlation between volumes and sex is still not clear: some authors observed significantly higher volumes in men than women,17–19 whereas others observed no significant relations between sex and SS volumes.8,16,20,21 Pirner et al. observed SS volumes significantly larger in men than in women for the left side and for the total volumes.3
Volumetric assessment through segmentation has also been used to evaluate the potential correlation between SS volume and protrusion of neurovascular structures: a positive correlation with internal carotid artery protrusion and simultaneous protrusion of internal carotid artery and optic nerve has been described, whereas no correlation was demonstrated with the only optic nerve protrusion.22
Regarding the correlation between sinus volumes and accessory septations, the data are discordant: Nejaim et al. described no significant difference,16 whereas Gibelli et al.23 stated that septations number significantly increased with sinus volume and that SS volumes were significantly higher in patients with internal carotid artery septal insertion.
No significant differences were observed in volume between patients with and without SS opacification.24
Volume segmentation to obtain 3D models of SS, followed by super-imposition, has been proposed as a new technique for human identification, due to the uniqueness of the SS.25
Maxillary sinus segmentation
Maxillary sinuses (MSs) are paired, pyramid-shaped structures (Figure 2), located in the middle of the face, and are the largest paranasal sinuses. Their function is extensively unknown; some authors have suggested a role in decreasing the weight of the skull, giving resonance to speech and warming inspired air.26
Figure 2.
Three-dimensional (3D) models of maxillary sinuses obtained from segmentation through ITK-SNAP.
In 15 patients, who had been submitted to both CT (Figure 3) and magnetic resonance (MR) studies, Andersen et al. observed the higher accuracy of MR dataset segmentation and the high repeatability of the procedure in both MR and CT analysis.27
Figure 3.
Segmentation procedure of the left maxillary sinus, with visualisation of the sinus in the axial, coronal and sagittal planes and of the obtained three-dimensional (3D) model.
Giacomini et al. developed an automated tool for quantifying the total and air-free volume of the MS, from CT datasets, to obtain standardised measurements. Linear regression and Bland–Altman tests demonstrated good agreement in comparison with manual segmentation.28
As for the SS, there is no concordance regarding volume differences according to sex in the MS: Cohen et al. observed significantly larger MSs in men than in women and in young patients;17 whereas Pirner et al. reported no significant differences according to sex.3
Statistically significant differences for the mean MS volume were found according to sex in fully dentate, partially edentulous and complete edentulous individuals, in a study on 276 patients segmented with Pro Plan CMF 1.4 software;29 the study also demonstrated significant variations of volumes according to the dentition status.
Pallanch et al., in CT manual segmentation of 48 patients with chronic rhino sinusitis, noted that volumetric scoring had a better correlation with disease severity compared with Lund–Mackay scoring.30 This result suggested a possible role of sinus volumetry in patients’ clinical management.
MS segmentation has also been used to confirm post-surgical volume changes after Le Fort I osteotomy on patients, affected by class II and class III malocclusions, who underwent two CBCT examinations, before the intervention, and at 6 months.31
The use of MS segmentation has also been proposed for sex estimation in forensic identification; in a study on 94 CBCT, ITK-SNAP was applied to get MS volumes through ONDemand 3D software, to determine a mathematical model for sex estimation; significantly larger volumes were demonstrated in men.14
Frontal sinus segmentation
Frontal sinuses (FSs) are paired irregularly shaped pneumatised cavities located in the frontal bone (Figure 4), usually asymmetric, characterised by a highly variable pattern of morphology, including the presence of septa and recesses, accessory cells (Figure 5).
Figure 4.
Three-dimensional (3D) model obtained from the segmentation of the frontal sinuses.
Figure 5.
(a) Coronal reconstruction of paranasal sinuses computed tomography (CT) scan with visualisation of a left frontal bullar cell (white arrow); (b) three-dimensional (3D) model of the relationship between the bullar cells and the frontal sinus, obtained from segmentation.
At FS segmentation (Figure 6), some studies observed a statistically significant difference between men and women for both left and right volumes,3,17 with higher volumes in men.3,17,32
Figure 6.
Segmentation procedure of frontal sinuses to calculate their volumes.
In a study of 173 CT scans, Jun et al. found a significant difference in the sinus volume according to sex and age, before it reached a maximum: the authors stated that FS pneumatisation proceeds till the third decade in men and second decade in women.33
FSs are interesting structures from a forensic point of view, due to the individuality in size and morphology.34 The extensive diffusion of CT examinations resulted in a huge availability of antemortem imaging data, useful for personal identification. Two approaches have been proposed: the first is based on CT segmentation according to the gray level, with a threshold value used for binarisation, followed by a region of interest placement and FS features extraction.35 This technique was applied to 310 CT datasets from 21 individuals, resulting in 77.25% of identification accuracy. The second type consists of FS segmentation to get 3D models that were imported on a 3D elaboration software (VAM) to perform registration and superimposition of models belonging to the same individuals (match) and to different individuals (mismatch), to calculate the average root mean square (RMS) point to point distance and obtain a chromatic map (Figure 7).36 RMS values were statistically significantly different when 3D models of different subjects were superimposed, therefore this procedure was proposed for human identification when antemortem imaging datasets are available.
Figure 7.
Application of the segmentation procedure with a forensic purpose: (a) and (b) show three-dimensional (3D) models of frontal sinuses belonging to two different subjects; (c) the superimposition of the two 3D models with VAM software; (d) comparison between the two 3D models: green highlights the concordant areas between the two models, whereas red, yellow and blue represent the discordant areas between the two models.
Ethmoidal sinus
The ethmoidal sinus (ES) is considered a part of the skull-base bones, rather than a true paranasal sinus, according to the evo-devo theory, and is markedly different from the other three paranasal sinuses.37
To the best of our knowledge, no study focused on the segmentation of the ES for the assessment of its features.
This fact can be explained by the irregular anatomy, high variability of these structures, but, most of all, to the difficulty in establishing universally accepted boundaries.3,17
Li et al. performed manual segmentation of the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, according to their significance in skull base and sinus surgery, to improve the efficiency of motion data clustering.37
Main segmentation studies and their results are listed in Supplementary files 1 and 2.
New perspectives
A relatively new application of CT segmentation is represented by the elaboration of 3D surface-shaded reconstruction showing the bone involvement of sinonasal anomalies42 and 3D printed anatomical models (Figure 8), suitable for education and didactics for otolaryngology and radiology residents,43–46 to prepare surgeons to approach intracranial lesions.47 The combination of virtual reality and 3D-printed models is feasible and may represent a useful educational tool.
Figure 8.
Segmentation of the maxillofacial bones, as a first step, to obtain printed three-dimensional (3D) models for educational purposes.
Two recently published articles demonstrated the feasibility of the automatic volumetric segmentation of the paranasal sinuses on CT datasets thanks to a convolutional network neural algorithm. This new segmentation technique allows the execution of and objective measurement of sinus volumes and opacification,48,49 and provides a severity index.48
The use of fully automated segmentation procedures and their technological development could provide helpful additional information for clinicians.
Conclusion
Segmentation of the paranasal sinuses on imaging datasets can be achieved with different procedures and provides volumetric information. Incorporation of this analysis into paranasal sinuses diagnostic assessment could result in additional information for researchers and clinicians.
Supplemental Material
Supplemental material, sj-pdf-1-neu-10.1177_1971400920946635 for Segmentation procedures for the assessment of paranasal sinuses volumes by Michaela Cellina, Daniele Gibelli, Annalisa Cappella, Tahereh Toluian, Carlo Valenti Pittino, Martinenghi Carlo and Giancarlo Oliva in The Neuroradiology Journal
Supplemental material, sj-pdf-2-neu-10.1177_1971400920946635 for Segmentation procedures for the assessment of paranasal sinuses volumes by Michaela Cellina, Daniele Gibelli, Annalisa Cappella, Tahereh Toluian, Carlo Valenti Pittino, Martinenghi Carlo and Giancarlo Oliva in The Neuroradiology Journal
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental material: Supplemental material for this article is available online.
ORCID iDs
Michaela Cellina https://orcid.org/0000-0002-7401-1971
Daniele Gibelli https://orcid.org/0000-0002-9591-1047
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Associated Data
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Supplementary Materials
Supplemental material, sj-pdf-1-neu-10.1177_1971400920946635 for Segmentation procedures for the assessment of paranasal sinuses volumes by Michaela Cellina, Daniele Gibelli, Annalisa Cappella, Tahereh Toluian, Carlo Valenti Pittino, Martinenghi Carlo and Giancarlo Oliva in The Neuroradiology Journal
Supplemental material, sj-pdf-2-neu-10.1177_1971400920946635 for Segmentation procedures for the assessment of paranasal sinuses volumes by Michaela Cellina, Daniele Gibelli, Annalisa Cappella, Tahereh Toluian, Carlo Valenti Pittino, Martinenghi Carlo and Giancarlo Oliva in The Neuroradiology Journal