An Automatic Method for PET Delineation of Cervical Tumors

Aim: PET imaging is increasingly utilized for radiation treatment planning. Nevertheless, accurate segmentation of PET images is a complex and unresolved problem. Aim of this work is the development of an automatic segmentation method of Biological Target Volume (BTV) in patients with cervical cancer. Materials and methods: Random walks (RW) is a graph-based method that represents a DICOM (Digital Imaging and COmmunications in Medicine) image as a graph. The voxels are its nodes and the edges are defined by a cost function which maps a change in image intensity to edge weights. Then, RW partitions the nodes into target and background subsets. To create an automatic method starting from previous work (A. Stefano, et al. A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study, in A. Petrosino, ed., Image Analysis and Processing - ICIAP 2013: LNCS, v. 8157, Springer Berlin Heidelberg, p. 711-720), we propose an automated RW seed localization approach. The algorithm identifies the PET slice with the highest SUVmax and a maximum of 10 target and 8 background seeds for each volume slice. The voxels with a SUV>95% of SUVmax are marked as target seeds. Then, the method explores the hottest voxel neighborhood through searching in 8 directions to identify the background voxels with a SUV