Selvi E.Selver M.A.Güzeliş C.Dicle O.2023-06-162023-06-1620141742-65881742-6596https://doi.org/10.1088/1742-6596/490/1/012079https://hdl.handle.net/20.500.14365/34922nd International Conference on Mathematical Modeling in Physical Sciences 2013, IC-MSQUARE 2013 -- 1 September 2013 through 5 September 2013 -- Prague -- 103396Segmentation of anatomical structures from medical image series is an ongoing field of research. Although, organs of interest are three-dimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme. To be able to use slice-by-slice techniques effectively, adjacent slice information, which represents likelihood of a region to be the structure of interest, plays critical role. Recent studies focus on using distance transform directly as a feature or to increase the feature values at the vicinity of the search area. This study presents a novel approach by constructing a higher order neural network, the input layer of which receives features together with their multiplications with the distance transform. This allows higher-order interactions between features through the non-linearity introduced by the multiplication. The application of the proposed method to 9 CT datasets for segmentation of the liver shows higher performance than well-known higher order classification neural networks. © Published under licence by IOP Publishing Ltd.eninfo:eu-repo/semantics/openAccessClassification (of information)Computerized tomographyImage segmentationMedical imagingNeural networksAnatomical structuresClinical applicationDistance transformsFeature valuesHigher order neural networkInput layersManual segmentationSegmentation performanceMedical image processingConference Object10.1088/1742-6596/490/1/0120792-s2.0-84896917056