%0 Conference Paper %1 ranveerjoyseereeoscaralfonsojimenezdeltoro2013using %A Joyseeree, Ranveer %A del Toro, Oscar Alfonso Jiménez %A Müller, Henning %B MICCAI MCV workshop %D 2014 %K myown segmentation visceral-project %T Using Probability Maps for Multi–organ Automatic Segmentation %X Organ segmentation is a vital task in diagnostic medicine. The ability to perform it automatically can save clinicians time and labor. In this paper, a method to achieve automatic segmentation of organs in three–dimensional (3D), non–annotated, full–body magnetic resonance (MR), and computed tomography (CT) volumes is proposed. According to the method, training volumes are registered to a chosen reference volume and the registration transform obtained is used to cre- ate an overlap volume for each annotated organ in the dataset. A 3D probability map, and its centroid, is derived from that. Afterwards, the reference volume is affinely mapped onto any non–annotated volume and the obtained mapping is applied to the centroid and the organ probability maps. Region–growing segmentation on the non–annotated volume may then be started using the warped centroid as the seed point and the warped probability map as an aid to the stopping criterion.

@inproceedings{ranveerjoyseereeoscaralfonsojimenezdeltoro2013using, abstract = {Organ segmentation is a vital task in diagnostic medicine. The ability to perform it automatically can save clinicians time and labor. In this paper, a method to achieve automatic segmentation of organs in three–dimensional (3D), non–annotated, full–body magnetic resonance (MR), and computed tomography (CT) volumes is proposed. According to the method, training volumes are registered to a chosen reference volume and the registration transform obtained is used to cre- ate an overlap volume for each annotated organ in the dataset. A 3D probability map, and its centroid, is derived from that. Afterwards, the reference volume is affinely mapped onto any non–annotated volume and the obtained mapping is applied to the centroid and the organ probability maps. Region–growing segmentation on the non–annotated volume may then be started using the warped centroid as the seed point and the warped probability map as an aid to the stopping criterion. }, added-at = {2013-11-05T13:33:42.000+0100}, author = {Joyseeree, Ranveer and del Toro, Oscar Alfonso Jiménez and Müller, Henning}, biburl = {https://www.bibsonomy.org/bibtex/243ca6ca55c112bf6777f60065163dfb4/ojimzt}, booktitle = {MICCAI MCV workshop}, interhash = {490d3fae93ca4263051cd91799daefb7}, intrahash = {43ca6ca55c112bf6777f60065163dfb4}, keywords = {myown segmentation visceral-project}, timestamp = {2014-10-28T22:41:38.000+0100}, title = {Using Probability Maps for Multi–organ Automatic Segmentation }, year = 2014 }