A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series
2nd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE), Prague, Czech Republic, 1 - 05 September 2013, vol.490, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 490
- Doi Number: 10.1088/1742-6596/490/1/012079
- City: Prague
- Country: Czech Republic
- Dokuz Eylül University Affiliated: Yes
Abstract
Segmentation 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.