Automatic segmentation of SPECT/CT-images

Introduction: The purpose of this study was to develop a technique for automatic segmentation based on Fourier descriptors for separating smooth objects from the background in SPECT-images. The aim of the segmentation method was to be able to automatically outline kidneys in SPECT-images of patients undergoing radionuclide therapy with 177 Lu-DOTATATE. The potential advantage of combining SPECT-and CT-images for the task of segmentation using the same technique was also explored. Material and methods: A method for segmentation of SPECT-and SPECT/CT-images was developed and implemented in IDL. The process included a first crude estimation of the organ position and size in a graphical user interface, producing an ellipsoid for initialisation of the coming optimisation procedure. The ellipsoid was used as input to an optimisation process where a surface, described by Fourier descriptors of successively increasing order, was adapted to a boundary measure based on the grey level gradient of the image. In order to avoid uncontrolled growth of the surface, the objective function was normalised to the total surface area raised to an exponent, termed the normalisation exponent. The surface description of the object could finally be transformed into a volume description of the object, i.e. a labelling of the voxels encompassed by the surface, by using a method based on the relative directions of the surface normal and the directed line segment joining a voxel coordinate and its closest point on the surface. The performance of the method was evaluated in different test images. Single organ images based on the left kidney of the XCAT anthropomorphic computer phantom were used in a first step, with different contrast to noise ratios and spatial resolutions. The degree of complexity was then increased by using test images designed to imitate the biodistribution of 177 Lu-DOTATATE in patients undergoing radionuclide therapy, again using the XCAT phantom. The same distribution was also used to create Monte Carlo simulated SPECT-images, to also model the actual camera characteristics. Finally the segmentation was performed in a set of real clinical images. In the simulated images the performance of the method was quantified both in terms of the distance between the estimated surface and the true surface, and in terms of a volume measure. In the single organ images evaluation was performed as a function of the number of Fourier orders used in the segmentation, for varying contrast to noise ratios and varying spatial resolution. Also investigated was the …