%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2019/09.13.20.39
%2 sid.inpe.br/sibgrapi/2019/09.13.20.39.14
%@doi 10.1109/SIBGRAPI.2019.00019
%T Fast and smart segmentation of paraspinal muscles in magnetic resonance imaging with CleverSeg
%D 2019
%A Ramos, Jonathan S.,
%A Cazzolato, Mirela T.,
%A Faiçal, Bruno S.,
%A Linares, Oscar A. C.,
%A Nogueira-Barbosa, Marcello H.,
%A Traina Jr., Caetano,
%A Traina, Agma J. M.,
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%@affiliation Ribeirão Preto Medical School (FMRP), University of São Paulo (USP)
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%@affiliation Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP)
%E Oliveira, Luciano Rebouças de,
%E Sarder, Pinaki,
%E Lage, Marcos,
%E Sadlo, Filip,
%B Conference on Graphics, Patterns and Images, 32 (SIBGRAPI)
%C Rio de Janeiro, RJ, Brazil
%8 28-31 Oct. 2019
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Segmentation, Muscle, MRI, CleverSeg.
%X Magnetic Resonance Imaging (MRI) is a non-invasive technique, which has been employed to detect and diagnose many spine pathologies. In a Computer-Aided Diagnosis(CAD) context, the segmentation of the paraspinal musculature from MRI may support measurement, quantification, and analysis of muscle-related pathologies. Current semi-automatic seg-mentation techniques require too much time from the physicians to annotate all slices in the exams. In this work, we focus on minimizing the time spent on manual annotation as well as on the overall segmentation processing time. We use the mean absolute error between slices aiming at minimizing the number of annotated slices in each exam. Moreover, we optimize the manual annotation time by estimating the inside annotation based on the outside annotation, while the competitors demand the annotation of inside and outside annotation (seeds). The experimental evaluation shows that our proposed approach is able to speed up the manual annotation process in up to 50%by annotating only a few representative slices, without loss of accuracy. By annotating only the outside region, the process can be further speed up by another 50%, reducing the total time to only 25% of the previously required. Thus, the total time spent on manual annotation is reduced by up to 75%, and, since human interaction is greatly diminished, allows a more productive and less tiresome activity. Despite that, our proposedCleverSeg method presented accuracy similar to or better than the competitors, while managing a faster processing time.
%@language en
%3 PaperID79.pdf