Toward automatic atlas-based surgical planning for septoplasty

Comparison of Anthropometric and Cephalometric Measurements Obtained by Stereophotogrammetry and 3D Computed Tomography of the Nose Before Septorhinoplasty

INTRODUCTION

Computed tomography (CT) is normally used in evaluation of patients with esthetic and functional nasal deformities. Stereophotogrammetry (SPG) is a measurement device that is an alternative to CT and does not harm human health. In this single-center retrospective study, we aimed to evaluate measurements obtained with CT and SPG.

METHODS

The measurements of 18 patients who applied to our clinic between January 2022 and August 2022 and planned for septorhinoplasty were performed on both 3D images obtained with paranasal sinus CT and SPG device (SLR type Vectra H1 system). Measurements included that dorsocolumellar length, columella-filtral length, nasal tip projection ratio (dorsocolumellar length/columella-filtral length), columella-labial angle, nasofrontal angle, tip deviation direction, tip deviation angle, tip deviation distance and dorsal nasal hump.

RESULTS

Most of patients were male (61.1%). Mean age was 24.5 years. Only columella-labial angle measurements showed a low level of significant difference (p < 0.05). However, there was no significance difference in other measurements (p > 0.05). A significant strong correlation was observed between all Vectra and CT measurements (p = 0.000).

CONCLUSION

SPG device can be applied quickly in polyclinic without giving radiation to patient. Measurements can be taken automatically using a software. Its use in postoperative period does not carry any risk. Disadvantage of SPG is lack of information about internal nasal passage. However, there is a strong correlation between measurements obtained from both measurement devices. Therefore, SPG can be considered as an alternative to CT imaging in operation planning.

LEVEL OF EVIDENCE IV

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Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts-an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

Purpose

Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.

Methods

The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).

Results

Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen.

Conclusion

In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-022-02673-5.