Literature DB >> 18383695

Geometrical model-based segmentation of the organs of sight on CT images.

György Bekes1, Eörs Máté, László G Nyúl, Attila Kuba, Márta Fidrich.   

Abstract

Segmentation of organs of sight such as the eyeballs, lenses, and optic nerves is a time consuming task for clinicians. The small size of the organs and the similar density of the surrounding tissues make the segmentation difficult. We developed a new algorithm to segment these organs with minimal user interaction. The algorithm needs only three seed points to fit an initial geometrical model to start an effective segmentation. The clinical evaluation shows that the output of our method is useful in clinical practice.

Mesh:

Year:  2008        PMID: 18383695     DOI: 10.1118/1.2826557

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

1.  Robust optic nerve segmentation on clinically acquired computed tomography.

Authors:  Robert L Harrigan; Swetasudha Panda; Andrew J Asman; Katrina M Nelson; Shikha Chaganti; Michael P DeLisi; Benjamin C W Yvernault; Seth A Smith; Robert L Galloway; Louise A Mawn; Bennett A Landman
Journal:  J Med Imaging (Bellingham)       Date:  2014-12-17

2.  Efficient orbital structures segmentation with prior anatomical knowledge.

Authors:  Nava Aghdasi; Yangming Li; Angelique Berens; Richard A Harbison; Kris S Moe; Blake Hannaford
Journal:  J Med Imaging (Bellingham)       Date:  2017-07-22

3.  Disambiguating the optic nerve from the surrounding cerebrospinal fluid: Application to MS-related atrophy.

Authors:  Robert L Harrigan; Andrew J Plassard; Frederick W Bryan; Gabriela Caires; Louise A Mawn; Lindsey M Dethrage; Siddharama Pawate; Robert L Galloway; Seth A Smith; Bennett A Landman
Journal:  Magn Reson Med       Date:  2015-03-07       Impact factor: 4.668

4.  An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images.

Authors:  Jack H Noble; Benoit M Dawant
Journal:  Med Image Anal       Date:  2011-05-12       Impact factor: 8.545

5.  Evaluation of Multi-Atlas Label Fusion for In Vivo MRI Orbital Segmentation.

Authors:  Swetasudha Panda; Andrew J Asman; Shweta P Khare; Lindsey Thompson; Louise A Mawn; Seth A Smith; Bennett A Landman
Journal:  J Med Imaging (Bellingham)       Date:  2014-07-18

6.  Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning.

Authors:  L Umapathy; B Winegar; L MacKinnon; M Hill; M I Altbach; J M Miller; A Bilgin
Journal:  AJNR Am J Neuroradiol       Date:  2020-05-21       Impact factor: 3.825

7.  A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

Authors:  Mikael Agn; Per Munck Af Rosenschöld; Oula Puonti; Michael J Lundemann; Laura Mancini; Anastasia Papadaki; Steffi Thust; John Ashburner; Ian Law; Koen Van Leemput
Journal:  Med Image Anal       Date:  2019-03-22       Impact factor: 8.545

8.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

9.  Robust Optic Nerve Segmentation on Clinically Acquired CT.

Authors:  Swetasudha Panda; Andrew J Asman; Michael P Delisi; Louise A Mawn; Robert L Galloway; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-21

10.  Real-Time MRI Reveals Unique Insight into the Full Kinematics of Eye Movements.

Authors:  Johannes Kirchner; Tamara Watson; Markus Lappe
Journal:  eNeuro       Date:  2022-01-07
  10 in total

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