Database-Guided Detection and Segmentation of Organs in MR FastView Localizers for Automatic Scan Planning

Neil Birkbeck¹, Nathan Lay¹, Jingdan Zhang¹, Artem Gritsenko¹, Jens Guehring², S. Kevin Zhou¹

We investigate automatic multi-organ localization in large FoV localizer datasets acquired using a fast continuously moving table technique (syngo TimCT FastView, Siemens AG, Erlangen, Germany). We developed a fast learning-based detection and segmentation method for 6 organs including liver, heart, lungs, and kidneys. The automatically identified anatomical information allows for precise automated scan planning based on an organ or structure of interest. We compare the accuracy of our detection and segmentation routines on ground truth annotations from 196 full body MR scout scans and achieve segmentation accuracy that are within the voxel spacing on average and are computed in under 8s.