Kidney Detection in 3-D Ultrasound Imagery via Shape-to-Volume Registration Based on Spatially Aligned Neural Network

This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D) ultrasound images. Using shape and texture priors, the proposed method automates the process of kidney detection, which is a problem of great importance in computer-assisted trauma diagnosis. This paper introduces a new complex-valued implicit shape model, which represents the multiregional structure of the kidney shape. A spatially aligned neural network classifiers with complex-valued output is designed to classify voxels into background and multiregional structure of the kidney shape. The complex values of the shape model and classification outputs are selected and incorporated in a new similarity metric, such as the shape-to-volume registration process only fits the shape model on the actual kidney shape in input ultrasound volumes. The algorithm's accuracy and sensitivity are evaluated using both simulated and actual 3-D ultrasound images, and it is compared against the performance of the state of the art. The results support the claims about accuracy and robustness of the proposed kidney detection method, and statistical analysis validates its superiority over the state of the art.