%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.22.23.46
%2 sid.inpe.br/sibgrapi/2016/07.22.23.46.34
%@doi 10.1109/SIBGRAPI.2016.045
%T Interactive Medical Image Segmentation by Statistical Seed Models
%D 2016
%A Spina, Thiago Vallin,
%A Martins, Samuel Botter,
%A Falcão, Alexandre Xavier,
%@affiliation Institute of Computing - University of Campinas
%@affiliation Institute of Computing - University of Campinas
%@affiliation Institute of Computing - University of Campinas
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Interactive Image Segmentation, Statistical Object Shape Models, Robot Users.
%X Interactive 3D object segmentation is an important and challenging activity in medical imaging, although it is tedious and error-prone to be done. Automatic segmentation methods aim to replace the user altogether, but require user interaction to produce training data sets of segmented masks and to make error corrections. We propose a complete framework for interactive medical image segmentation, which reduces user effort by automatically providing an initial segmentation result. We develop a Statistical Seed Model (SSM) to this end, that improves from seed sets selected by robot users when reconstructing masks of previously segmented images. The SSM outputs a seed set that may be used to automatically delineate a new test image. The seeds provide both an implicit object shape constraint and a flexible way of interactively correcting segmentation. We demonstrate that our framework decreases the amount of user interaction by a factor of three, when segmenting MR-images of the cerebellum.
%@language en
%3 PID4373563.pdf