Matlab® toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling

Keustermans, William; Huysmans, Toon; Schmelzer, B; Sijbers, Jan; Dirckx, Joris J.J.

doi:10.1016/j.compbiomed.2018.12.008

Cited by 10 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a landmark-based statistical shape model technique that applies anatomical knowledge to form a set of training data in order to obtain a segmentation of determined structures, such as the nasal cavities, decreasing the manual intervention time required for the operation. 45 Huang et al elaborated on an automatic segmentation in order to obtain nasal models from CT for the analysis of CFD, showing high segmentation accuracy and an average distance error of 0.3 mm. 40…”

Section: Segmentation Analysismentioning

confidence: 99%

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

et al. 2020

View full text Add to dashboard Cite

The nasal cavities are complex anatomical structures with high inter-individual variability that relates to different functions. Different anatomic variants may manifest at this site, mainly belonging to the nasal septum and turbinates. Precise knowledge of the anatomy and variants is fundamental for both radiologists and ENT surgeons. This article provides an overview of the main anatomic variants and their frequency, according to the existing literature, as well as ongoing research on nasal cavity segmentation in order to obtain personal 3D models and to predict post-surgical results.

show abstract

Section: Segmentation Analysismentioning

confidence: 99%

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Second, a statistical shape model (SSM) computes the mean shape and the shape variations of the maxillary sinuses and nasal cavities. SSMs are widely used in medical image processing, such as image segmentation, 16 image registration 2 and image reconstruction. 10 SSMs allows to quantify geometrical variations and features from various shapes and anatomical structures.…”

Section: Introductionmentioning

confidence: 99%

“…Till date the studies on SSMs of paranasal sinuses essentially focused on automatic image segmentation. 16,25 There is up to now, to the best of the author's knowledge, no study that uses an SSM to guide the design procedure for steerable flexible surgical instruments. Third, design-oriented metrics are selected based on the SSM.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on 3D Shape Analysis Towards the Design of Flexible Instruments for Endoscopic Maxillary Sinus Surgery

et al. 2021

View full text Add to dashboard Cite

The emergence of steerable flexible instruments have widened the uptake of minimally invasive surgical techniques. In sinus surgery, such flexible instruments could increase the access to difficult-to-reach anatomical areas. However, design-oriented metrics, essential for the development of steerable flexible instruments for maxillary sinus surgery, are lacking. This paper proposes a method to process measurements and provide the instrument designer with essential information to develop adapted flexible instruments for limited access surgery. The method was applied to maxillary sinus surgery and showed that an instrument with a diameter smaller than 2.4 mm could be used on more than 72.5 % of the subject set. Provided that this flexible instrument can bend up to 164.4 • , all areas within the maxillary sinus could be reached through a regular antrostomy without resorting to extra incision or tissue removal in 94.9 % of the population set.

show abstract

“…Figure 1 exemplifies the close proximity of the paranasal sinuses to the nasal cavity and highlights the connectivity between the two regions. The majority of existing nasal segmentation methods have either included nearby airway components as part of the segmentation [12][13][14][15][16][17][18], or require some form of manual intervention in order to derive results [17][18][19][20][21][22]. In the work of Bui et al [12], a multi-step level-set segmentation procedure was utilised to automatically segment the nasal cavity and the surrounding paranasal sinuses from cone-beam computed tomography (CBCT).…”

Section: Introductionmentioning

confidence: 99%

“…This is evident among studies that focused on segmenting just the nasal cavity itself, such as in the works of Kimura et al [20] and Alsufyani et al [22] where thresholding was used to segment the upper airway, and large amounts of manual delineation was required in order to separate the nasal cavity from the rest of the airway regions. Keustermans et al [21] made use of an active shape model (ASM) to semi-automatically segment the nasal cavity. An ASM is a landmark based statistical shape model (SSM) segmentation method which makes use of anatomical knowledge derived from a set of training data to segment a particular organ or structure [23,24].…”

Section: Introductionmentioning

confidence: 99%

An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

Huang

Nedanoski

Fletcher

et al. 2019

Computers in Biology and Medicine

View full text Add to dashboard Cite

The use of computational fluid dynamics (CFD) to model and predict surgical outcomes in the nasal cavity is becoming increasingly popular. Despite a number of well-known nasal segmentation methods being available, there is currently a lack of an automated, CFD targeted segmentation framework to reliably compute accurate patient-specific nasal models. This paper demonstrates the potential of a robust nasal cavity segmentation framework to automatically segment and produce nasal models for CFD. The framework was evaluated on a clinical dataset of 30 head Computer Tomography (CT) scans, and the outputs of the segmented nasal models were further compared with ground truth models in CFD simulations on pressure drop and particle deposition efficiency. The developed framework achieved a segmentation accuracy of 90.9 DSC, and an average distance error of 0.3 mm. Preliminary CFD simulations revealed similar outcomes between using ground truth and segmented models. Additional analysis still needs to be 37 conducted to verify the accuracy of using segmented models for CFD purposes.

show abstract

Matlab® toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling

Cited by 10 publications

References 37 publications

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

Nasal cavities and the nasal septum: Anatomical variants and assessment of features with computed tomography

A Method Based on 3D Shape Analysis Towards the Design of Flexible Instruments for Endoscopic Maxillary Sinus Surgery

An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography

Contact Info

Product

Resources

About