A New Shape-Based Segmentation Approach for the DCE-MRI Kidney Images

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) is considered. The algorithm is based on segmentation to isolate the kidney from the surrounding anatomical structures via a shape-based segmentation approach using level sets. So the main focus of this paper is the shape based segmentation. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporate the image information with the shape prior in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. The perfusion curves that show the transportation of the contrast agent into the tissue are obtained from segmented kidneys and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction.