@InProceedings{8903109,
  author = {F. Fallah and K. Armanious and B. Yang and F. Bamberg},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Volumetric Surface-guided Graph-based Segmentation of Cardiac Adipose Tissues on Fat-Water MR Images},
  year = {2019},
  pages = {1-5},
  abstract = {Different endocrine roles of cardiac adipose tissues motivate the analysis of their volumes and compositions on large cohort image data sets. This, however, demands reliable robust methods for automated segmentations as manual segmentations are tedious costly and unreproducible. Besides the effects of noise and partial volumes, segmentation of these adipose tissues on clinical medical images is challenged by their similar intensities and features and undetectability of their boundaries. In this paper, we present a feature- and prior-based random walker graph that additionally incorporates a diffusion-based susceptible infected recovered model to guide the segmentation by the curvatures of the surface of the segmented cardiac structures. This method is trained and evaluated for segmenting epicardial, pericardial, and perivascular adipose tissues on volumetric fat-water magnetic resonance images. The obtained results demonstrate its utility for large cohort investigation of these adipose compartments and also any other segmentation task on multichannel images.},
  keywords = {biological tissues;biomedical MRI;cardiology;fats;image segmentation;medical image processing;physiological models;random processes;volumetric surface-guided graph-based segmentation;cardiac adipose tissues;fat-water MR images;cohort image data sets;partial volumes;clinical medical images;random walker graph;segmented cardiac structures;epicardial adipose tissues;perivascular adipose tissues;fat-water magnetic resonance images;adipose compartments;multichannel images;pericardial adipose tissues;diffusion-based susceptible infected recovered model;endocrine roles;Image segmentation;Fats;Training;Signal processing;Motion segmentation;Histograms;Myocardium;Random Walker Algorithm;Feature and Prior Learning;Diffusion-based Susceptible Infected Recovered Model;Surface Curvature;Cardiac Adipose Tissues},
  doi = {10.23919/EUSIPCO.2019.8903109},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529417.pdf},
}

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This, however, demands reliable robust methods for automated segmentations as manual segmentations are tedious costly and unreproducible. Besides the effects of noise and partial volumes, segmentation of these adipose tissues on clinical medical images is challenged by their similar intensities and features and undetectability of their boundaries. In this paper, we present a feature- and prior-based random walker graph that additionally incorporates a diffusion-based susceptible infected recovered model to guide the segmentation by the curvatures of the surface of the segmented cardiac structures. This method is trained and evaluated for segmenting epicardial, pericardial, and perivascular adipose tissues on volumetric fat-water magnetic resonance images. The obtained results demonstrate its utility for large cohort investigation of these adipose compartments and also any other segmentation task on multichannel images.","keywords":"biological tissues;biomedical MRI;cardiology;fats;image segmentation;medical image processing;physiological models;random processes;volumetric surface-guided graph-based segmentation;cardiac adipose tissues;fat-water MR images;cohort image data sets;partial volumes;clinical medical images;random walker graph;segmented cardiac structures;epicardial adipose tissues;perivascular adipose tissues;fat-water magnetic resonance images;adipose compartments;multichannel images;pericardial adipose tissues;diffusion-based susceptible infected recovered model;endocrine roles;Image segmentation;Fats;Training;Signal processing;Motion segmentation;Histograms;Myocardium;Random Walker Algorithm;Feature and Prior Learning;Diffusion-based Susceptible Infected Recovered Model;Surface Curvature;Cardiac Adipose Tissues","doi":"10.23919/EUSIPCO.2019.8903109","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529417.pdf","bibtex":"@InProceedings{8903109,\n author = {F. Fallah and K. Armanious and B. Yang and F. Bamberg},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Volumetric Surface-guided Graph-based Segmentation of Cardiac Adipose Tissues on Fat-Water MR Images},\n year = {2019},\n pages = {1-5},\n abstract = {Different endocrine roles of cardiac adipose tissues motivate the analysis of their volumes and compositions on large cohort image data sets. This, however, demands reliable robust methods for automated segmentations as manual segmentations are tedious costly and unreproducible. Besides the effects of noise and partial volumes, segmentation of these adipose tissues on clinical medical images is challenged by their similar intensities and features and undetectability of their boundaries. In this paper, we present a feature- and prior-based random walker graph that additionally incorporates a diffusion-based susceptible infected recovered model to guide the segmentation by the curvatures of the surface of the segmented cardiac structures. This method is trained and evaluated for segmenting epicardial, pericardial, and perivascular adipose tissues on volumetric fat-water magnetic resonance images. 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