Neil Birkbeck1, Nathan Lay1, Jingdan Zhang1, Artem Gritsenko1, Jens Guehring2, S. Kevin Zhou1
1Imaging & Computer Vision, Siemens Corp., Corporate Technology, Princeton, NJ, United States; 2Imaging & Therapy Division, Siemens Healthcare, Erlangen, Germany
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.