3D Segmentation for Multi-Organs in CT Images

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3D Segmentation for Multi-Organs in CT Images
Bajger, Mariusz (Flinders University (Australia). Medical Devices Research Institute)
Lee, Gobert (Flinders University (Australia). School of Computer Science, Engineering and Mathematics)
Caon, Martin (Flinders University (Australia). School of Nursing and Midwifery)

Fecha:	2013
Resumen:	The study addresses the challenging problemof automatic segmentation of the human anatomy needed for radiation dose calculations. Three-dimensional extensions of two well-known stateof- the art segmentation techniques are proposed and tested for usefulness on a set of clinical CT images. The new techniques are 3D Statistical Region Merging (3D-SRM) and 3D Efficient Graph-based Segmentation (3D-EGS). Segmentations of eight representative tissues (lungs, stomach, liver, heart, kidneys, spleen, bones and the spinal cord) were tested for accuracy using the Dice index, the Hausdorff distance and the Ht index. The 3D-SRM outperformed 3D-EGS producing the average (across the 8 tissues) Dice index, the Hausdorff distance, and the H2 of 0. 89, 12. 5 mm and 0. 93, respectively.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Voxel model ; Image segmentation ; Statistical region merging ; Efficient graph-based ; Segmentation ; Full-body CT
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 12, Núm. 2 (2013) , p. 13-27, ISSN 1577-5097

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Registro creado el 2013-11-11, última modificación el 2021-12-11