Anatomical structures segmentation by spherical 3D ray casting gradient domain editing

A. Kronman; Leo Joskowicz; J. Sosna

doi:10.1007/978-3-642-33418-4_45

Anatomical structures segmentation by spherical 3D ray casting gradient domain editing

A. Kronman, Leo Joskowicz, J. Sosna

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.

Original language	American English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
Editors	Zhuowen Tu, Bjoern H. Menze, Nicholas Ayache, Hervé Delingette, Antonio Criminisi, Bjoern H. Menze, Le Lu, Georg Langs, Albert Montillo, Georg Langs, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	363-370
Number of pages	8
ISBN (Print)	9783642334177
DOIs	https://doi.org/10.1007/978-3-642-33418-4_45
State	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 5 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	5/10/12 → 5/10/12

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.

Access to Document

10.1007/978-3-642-33418-4_45

Cite this

Kronman, A., Joskowicz, L., & Sosna, J. (2012). Anatomical structures segmentation by spherical 3D ray casting gradient domain editing. In Z. Tu, B. H. Menze, N. Ayache, H. Delingette, A. Criminisi, B. H. Menze, L. Lu, G. Langs, A. Montillo, G. Langs, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 363-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33418-4_45

Kronman, A. ; Joskowicz, Leo ; Sosna, J. / Anatomical structures segmentation by spherical 3D ray casting gradient domain editing. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Zhuowen Tu ; Bjoern H. Menze ; Nicholas Ayache ; Hervé Delingette ; Antonio Criminisi ; Bjoern H. Menze ; Le Lu ; Georg Langs ; Albert Montillo ; Georg Langs ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 363-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.",

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Kronman, A, Joskowicz, L & Sosna, J 2012, Anatomical structures segmentation by spherical 3D ray casting gradient domain editing. in Z Tu, BH Menze, N Ayache, H Delingette, A Criminisi, BH Menze, L Lu, G Langs, A Montillo, G Langs, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 363-370, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 5/10/12. https://doi.org/10.1007/978-3-642-33418-4_45

Anatomical structures segmentation by spherical 3D ray casting gradient domain editing. / Kronman, A.; Joskowicz, Leo ; Sosna, J.
Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Zhuowen Tu; Bjoern H. Menze; Nicholas Ayache; Hervé Delingette; Antonio Criminisi; Bjoern H. Menze; Le Lu; Georg Langs; Albert Montillo; Georg Langs; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 363-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Anatomical structures segmentation by spherical 3D ray casting gradient domain editing

AU - Kronman, A.

AU - Joskowicz, Leo

AU - Sosna, J.

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2012.

PY - 2012

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N2 - Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.

AB - Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings

A2 - Tu, Zhuowen

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A2 - Delingette, Hervé

A2 - Criminisi, Antonio

A2 - Menze, Bjoern H.

A2 - Lu, Le

A2 - Langs, Georg

A2 - Montillo, Albert

A2 - Langs, Georg

A2 - Golland, Polina

A2 - Mori, Kensaku

PB - Springer Verlag

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Kronman A, Joskowicz L , Sosna J. Anatomical structures segmentation by spherical 3D ray casting gradient domain editing. In Tu Z, Menze BH, Ayache N, Delingette H, Criminisi A, Menze BH, Lu L, Langs G, Montillo A, Langs G, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 363-370. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33418-4_45

Anatomical structures segmentation by spherical 3D ray casting gradient domain editing

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