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

Cone-Beam Computed Tomography for Objective Diagnosis of Age-Related Soft Tissue Changes in Lower Face and Neck

BACKGROUND

An objective assessment of the causes of age-related contour deformities of the soft tissues of the face and neck is very important in esthetic surgery, especially as minimally invasive techniques gain increasing popularity.

METHODS

To visualize the tissues that cause age-related soft tissue changes, we performed cone-beam computed tomography (CBCT) in 37 patients who underwent facial and neck rejuvenation procedures in 2021-2022.

RESULTS

Vertical CBCT enabled visualization of the causes and degree of tissue involvement in age-related changes in the lower third of the face and neck. CBCT showed the location and condition of the platysma (hypo- [ptosis], normo-, or hyper-tonus), position, thickness, and location (above and/or below the platysma) of fat tissue, presence of ptosis of the submandibular salivary glands, condition of the anterior bellies of the digastric muscles, and the degree of their participation in contours of the cervicomandibular angle, and location of the hyoid bone. Moreover, CBCT enabled demonstrating for the patient the facial and neck contour deformations and discussing the suggested corrective methods using a clear objective visual image.

CONCLUSIONS

CBCT in the upright position enables objective assessment of each soft tissue in the age-related deformity of the cervicofacial region and provides an opportunity to plan the appropriate impact on the particular anatomical structures during rejuvenation procedures and estimate their results. This is the only study to date to objectively and clearly visualize the entire topographic anatomy of the soft tissues of the face and neck vertically for plastic surgeons and patients.

LEVEL OF EVIDENCE IV

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Morphological and Metrical Relationships Between Sphenoid Sinus and Sella Turcica: Possible Applications in Transnasal Transsphenoidal Surgery

INTRODUCTION

Transsphenoidal surgeries imply the risk of intraoperative lesions to the neurovascular structures surrounding the sphenoid sinus (SS). Aim of the present study is to assess the metrical and morphologic relationships existing between SS and sella turcica (ST).

MATERIALS AND METHODS

Two hundred computed tomography-scans of patients were selected. For each patient volumes of SS were calculated from their 3-dimensional models segmented through ITK-SNAP program. Variants of SS in pneumatisation and sellar diameters [antero-posterior (AP) diameter, depth, and length] were evaluated on each computed tomography-scan. Correlations among different measurements were assessed through Spearman test ( P <0.01), whereas associations between sellar parameters and presence of pneumatisation variants were assessed through Mann-Whitney test ( P <0.01).

RESULTS

In males, pneumatization of the greater wings was related to smaller AP diameter ( P <0.01) and depth of ST ( P <0.01), whereas in females lower values of depth were found in patients with pneumatization of the pterygoid processes ( P <0.01). In both sexes, a positive correlation was found between AP diameter and, respectively, length and depth of ST ( P <0.01), together with a negative correlation between volume of SS and depth of ST ( P <0.01). Lastly, in females a positive correlation was found between age and, respectively, length and depth of ST ( P <0.01).

CONCLUSIONS

The present study highlighted new metrical and morphologic relationships between volume and pneumatisation of SS and diameters of ST. Knowledge of these correlations allows to understand more clearly, in the preoperative setting, the surgical working space. Further studies are needed, especially for what concerns the relationship between sellar measurements and age in females.

Volumetric measurement of paranasal sinuses and its clinical significance in pituitary neuroendocrine tumors operated using an endoscopic endonasal approach

Objective

Endoscopic endonasal surgery (EES) for deep intracranial lesions has gained popularity following recent developments in endoscopic technology. The operability of invasive pituitary neuroendocrine tumors (PitNETs) depends on the anatomy of the nasal cavity and paranasal sinus. This study aimed to establish a simple volume reconstruction algorithm of the nasal cavity and paranasal sinus. Additionally, this is the first study to demonstrate the relationship between the segmentation method and the clinical significance in patients with PitNET.

Methods

Pre-and postoperative tumor volumes were analyzed in 106 patients with primary (new-onset) PitNETs (80 nonfunctioning and 26 functioning) who underwent EES. The efficiency and accuracy of the semiautomatic segmentation with manual adjustments (SSMA) method was compared with other established segmentation methods for volumetric analysis in the nasal cavity and paranasal sinuses. Correlations between the measured nasal cavity and paranasal sinus volumes and the extent of tumor removal were evaluated.

Results

The SSMA method yielded accurate and time-saving results following the volumetric analyses of nasal cavity and paranasal sinuses with complex structures. Alternatively, the manual and semiautomatic segmentation methods proved time-consuming and inaccurate, respectively. The sphenoid sinus volume measured by SSMA was significantly correlated with the extent of tumor removal in patients with nonfunctioning Knosp grade 3 and 4 PitNET (r = 0.318; p = 0.015).

Conclusion

The volume of sphenoid sinus potentially could predict the extent of resection due to better visualization of the tumor for PitNETs with CS invasion.

A volumetric study of mandibular condyles in orthognathic patients by semiautomatic segmentation

Purpose

This study was conducted to elucidate volumetric data of mandibular condyles of orthognathic patients by analyzing cone beam computed tomography images based upon semiautomatic segmentation.

Methods

Cone beam computed tomography images of 87 patients with malocclusions were analyzed in this retrospective study. Patients were between 17 and 53 years old and diagnosed with Angle class I, II, or III malocclusion. By using the validated open-source software “ITK-SNAP,” the volumetric measurements of 174 mandibular condyles were performed. Volumetric analysis was performed according to intra-subject side differences by paired Student t test. In accordance to inter-subject side, gender, age and type of malocclusion differences bivariate analysis and ANOVA were applied.

Results

The mean volume for the right condyle was 1.378 ± 0.447 cm³, with a maximum of 2.379 cm³ and a minimum of 0.121 cm³. The mean volume for the left side was 1.435 ± 0.474 cm³, with a maximum of 3.264 cm³ and a minimum of 0.109 cm³. Bivariate analysis indicated a highly significant inter-subject difference between the volume of the left and right mandibular condyles (p < 0.01). Females had a significantly smaller condyle volume than males (p < 0.05 left condyle; p < 0.01 right condyle).

Conclusion

The fact that shape and volume of mandibular condyles show a high susceptibility to pathological alterations and particularly malocclusions makes a precise knowledge about volumetric changes indispensable. Our results show that significant inter-subject differences in condyle volume could be found with respect to the side and gender. Larger volumes could be assessed for the left condyle and for male patients.

Deep Active Learning for Automatic Segmentation of Maxillary Sinus Lesions Using a Convolutional Neural Network

The aim of this study was to segment the maxillary sinus into the maxillary bone, air, and lesion, and to evaluate its accuracy by comparing and analyzing the results performed by the experts. We randomly selected 83 cases of deep active learning. Our active learning framework consists of three steps. This framework adds new volumes per step to improve the performance of the model with limited training datasets, while inferring automatically using the model trained in the previous step. We determined the effect of active learning on cone-beam computed tomography (CBCT) volumes of dental with our customized 3D nnU-Net in all three steps. The dice similarity coefficients (DSCs) at each stage of air were 0.920 ± 0.17, 0.925 ± 0.16, and 0.930 ± 0.16, respectively. The DSCs at each stage of the lesion were 0.770 ± 0.18, 0.750 ± 0.19, and 0.760 ± 0.18, respectively. The time consumed by the convolutional neural network (CNN) assisted and manually modified segmentation decreased by approximately 493.2 s for 30 scans in the second step, and by approximately 362.7 s for 76 scans in the last step. In conclusion, this study demonstrates that a deep active learning framework can alleviate annotation efforts and costs by efficiently training on limited CBCT datasets.