Literature DB >> 32757847

Segmentation procedures for the assessment of paranasal sinuses volumes.

Michaela Cellina1, Daniele Gibelli2, Annalisa Cappella2, Tahereh Toluian3, Carlo Valenti Pittino3, Martinenghi Carlo4, Giancarlo Oliva1.   

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.

Entities:  

Keywords:  3D; Paranasal sinuses; computer application; segmentation; volumes

Mesh:

Year:  2020        PMID: 32757847      PMCID: PMC7868593          DOI: 10.1177/1971400920946635

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  46 in total

1.  A preliminary 3D computed tomography study of the human maxillary sinus and nasal cavity.

Authors:  Lauren N Butaric; Robert C McCarthy; Douglas C Broadfield
Journal:  Am J Phys Anthropol       Date:  2010-11       Impact factor: 2.868

2.  Use of 3D-Printed and 2D-Illustrated International Frontal Sinus Anatomy Classification Anatomic Models for Resident Education.

Authors:  Christopher M Low; Jonathan M Morris; Jane S Matsumoto; Janalee K Stokken; Erin K O'Brien; Garret Choby
Journal:  Otolaryngol Head Neck Surg       Date:  2019-07-09       Impact factor: 3.497

3.  Relationship between sphenoid sinus volume and accessory septations: A 3D assessment of risky anatomical variants for endoscopic surgery.

Authors:  Daniele Gibelli; Michaela Cellina; Stefano Gibelli; Annalisa Cappella; Antonio Giancarlo Oliva; Giovanni Termine; Chiarella Sforza
Journal:  Anat Rec (Hoboken)       Date:  2019-10-02       Impact factor: 2.064

4.  Accuracy and precision of manual segmentation of the maxillary sinus in MR images-a method study.

Authors:  Tobias N Andersen; Tron A Darvann; Shumei Murakami; Per Larsen; Yurie Senda; Anders Bilde; Christian V Buchwald; Sven Kreiborg
Journal:  Br J Radiol       Date:  2018-03-20       Impact factor: 3.039

5.  Automatic Segmentation of the Paranasal Sinus from Computer Tomography Images Using a Probabilistic Atlas and a Fully Convolutional Network.

Authors:  Yutaro Iwamoto; Kun Xiong; Takahiro Kitamura; Xian-Hua Han; Naoki Matsushiro; Hiroshi Nishimura; Yen-Wei Chen
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2019-07

6.  The analysis of maxillary sinus aeration according to aging process; volume assessment by 3-dimensional reconstruction by high-resolutional CT scanning.

Authors:  Beom-Cho Jun; Sun-Wha Song; Chan-Soon Park; Dong-Hee Lee; Kwang-Jae Cho; Jin-Hee Cho
Journal:  Otolaryngol Head Neck Surg       Date:  2005-03       Impact factor: 3.497

7.  Automatic frontal sinus recognition in computed tomography images for person identification.

Authors:  Luis A de Souza; Aparecido N Marana; Silke A T Weber
Journal:  Forensic Sci Int       Date:  2018-03-23       Impact factor: 2.395

8.  Analysis of manual segmentation in paranasal CT images.

Authors:  Kathrin Tingelhoff; Klaus W G Eichhorn; Ingo Wagner; Maria E Kunkel; Analia I Moral; Markus E Rilk; Friedrich M Wahl; Friedrich Bootz
Journal:  Eur Arch Otorhinolaryngol       Date:  2008-02-06       Impact factor: 2.503

9.  Computed tomography measurements of different dimensions of maxillary and frontal sinuses.

Authors:  Pernilla Sahlstrand-Johnson; Magnus Jannert; Anita Strömbeck; Kasim Abul-Kasim
Journal:  BMC Med Imaging       Date:  2011-04-05       Impact factor: 1.930

10.  Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus.

Authors:  Guilherme Giacomini; Ana Luiza Menegatti Pavan; João Mauricio Carrasco Altemani; Sergio Barbosa Duarte; Carlos Magno Castelo Branco Fortaleza; José Ricardo de Arruda Miranda; Diana Rodrigues de Pina
Journal:  PLoS One       Date:  2018-01-05       Impact factor: 3.240

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  3 in total

1.  MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.

Authors:  Dominik Müller; Frank Kramer
Journal:  BMC Med Imaging       Date:  2021-01-18       Impact factor: 1.930

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

Authors:  Nermin Morgan; Adriaan Van Gerven; Andreas Smolders; Karla de Faria Vasconcelos; Holger Willems; Reinhilde Jacobs
Journal:  Sci Rep       Date:  2022-05-07       Impact factor: 4.996

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

Authors:  Erli Sarilita; Yurika Ambar Lita; Harry Galuh Nugraha; Nani Murniati; Harmas Yazid Yusuf
Journal:  Anat Cell Biol       Date:  2021-12-31
  3 in total

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