Literature DB >> 18253744

Analysis of manual segmentation in paranasal CT images.

Kathrin Tingelhoff1, Klaus W G Eichhorn, Ingo Wagner, Maria E Kunkel, Analia I Moral, Markus E Rilk, Friedrich M Wahl, Friedrich Bootz.   

Abstract

Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.

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Mesh:

Year:  2008        PMID: 18253744     DOI: 10.1007/s00405-008-0594-z

Source DB:  PubMed          Journal:  Eur Arch Otorhinolaryngol        ISSN: 0937-4477            Impact factor:   2.503


  4 in total

1.  A CT study of the course of growth of the maxillary sinus: normal subjects and subjects with chronic sinusitis.

Authors:  A Ikeda; M Ikeda; A Komatsuzaki
Journal:  ORL J Otorhinolaryngol Relat Spec       Date:  1998 May-Jun       Impact factor: 1.538

2.  A methodology for evaluation of boundary detection algorithms on medical images.

Authors:  V Chalana; Y Kim
Journal:  IEEE Trans Med Imaging       Date:  1997-10       Impact factor: 10.048

3.  Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI.

Authors:  Edward A Ashton; Chihiro Takahashi; Michel J Berg; Andrew Goodman; Saara Totterman; Sven Ekholm
Journal:  J Magn Reson Imaging       Date:  2003-03       Impact factor: 4.813

4.  Navigated control in functional endoscopic sinus surgery.

Authors:  G Strauss; K Koulechov; R Richter; A Dietz; C Trantakis; T Lüth
Journal:  Int J Med Robot       Date:  2005-09       Impact factor: 2.547

  4 in total
  8 in total

1.  CT-based manual segmentation and evaluation of paranasal sinuses.

Authors:  S Pirner; K Tingelhoff; I Wagner; R Westphal; M Rilk; F M Wahl; F Bootz; Klaus W G Eichhorn
Journal:  Eur Arch Otorhinolaryngol       Date:  2008-08-21       Impact factor: 2.503

2.  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

Review 3.  Segmentation procedures for the assessment of paranasal sinuses volumes.

Authors:  Michaela Cellina; Daniele Gibelli; Annalisa Cappella; Tahereh Toluian; Carlo Valenti Pittino; Martinenghi Carlo; Giancarlo Oliva
Journal:  Neuroradiol J       Date:  2020-08-06

4.  Two- and three-dimensional anatomy of paranasal sinuses in Arabian foals.

Authors:  Sadullah Bahar; Durmus Bolat; Mustafa Orhun Dayan; Yahya Paksoy
Journal:  J Vet Med Sci       Date:  2013-09-05       Impact factor: 1.267

5.  Sensitivity of nasal airflow variables computed via computational fluid dynamics to the computed tomography segmentation threshold.

Authors:  Giancarlo B Cherobin; Richard L Voegels; Eloisa M M S Gebrim; Guilherme J M Garcia
Journal:  PLoS One       Date:  2018-11-16       Impact factor: 3.240

6.  OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images.

Authors:  Gavrielle R Untracht; Rolando S Matos; Nikolaos Dikaios; Mariam Bapir; Abdullah K Durrani; Teemapron Butsabong; Paola Campagnolo; David D Sampson; Christian Heiss; Danuta M Sampson
Journal:  PLoS One       Date:  2021-12-09       Impact factor: 3.240

7.  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

8.  Evaluation of inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions.

Authors:  Ali Ataei; Florieke Eggermont; Milan Baars; Yvette van der Linden; Jacky de Rooy; Nico Verdonschot; Esther Tanck
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-07-15       Impact factor: 2.924

  8 in total

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