A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Selvi, Es¸ref^1 ; Selver, M. Alper^2 ; Güzelis¸, Cüneyt^3 ; Dicle, Ouz^4

Dokuz Eylül University, Institute of Natural and Applied Sciences, Buca, Izmir, Turkey^1

Dokuz Eylül University, Department of Electrical and Electronics Engineering, Buca, Izmir, Turkey^2

Izmir University of Economics, Department of Electrical and Electronics Engineering, Izmir, Turkey^3

Dokuz Eylül University, Faculty of Medicine, Department of Radiology, Balcova, Izmir, Turkey^4

关键词: Anatomical structures;    Clinical application;    Distance transforms;    Feature values;    Higher order neural network;    Input layers;    Manual segmentation;    Segmentation performance;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012079/pdf
DOI  :  10.1088/1742-6596/490/1/012079

来源: IOP

【 摘 要 】

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.