TY - CPAPER AB - Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans. AU - Valindria,V AU - Lavdas,I AU - Cerrolaza,J AU - Aboagye,EO AU - Rockall,A AU - Rueckert,D AU - Glocker,B DO - 10.1007/978-3-030-00919-9_40 EP - 354 PB - Springer Verlag PY - 2018/// SN - 0302-9743 SP - 346 TI - Small organ segmentation in whole-body MRI using a two-stage FCN and weighting schemes UR - http://dx.doi.org/10.1007/978-3-030-00919-9_40 UR - http://hdl.handle.net/10044/1/62649 ER -