Deviations from the MR acquisition guidelines could lead to images with serious quality concerns such as incompletely imaged anatomies, which might require re-examinations and could result in missed pathologies. In this paper, we propose a deep learning method to automatically estimate the coverage of the target anatomy and to predict the extent of an anatomy outside the present field-of-view (FOV). For this purpose, we employed a 3D fully-convolutional neural network operating at multiple resolution levels. The proposed solution could be employed to propose a correct FOV setting in case of organ-coverage issues while patient is on the table and could be incorporated as a retrospective tool for quality monitoring and staff training. Our method was evaluated for four abdominal organs - liver, spleen, and left and right kidneys - in 40 magnetic resonance (MR) images from the publicly available Combined Healthy Abdominal Organ Segmentation (CHAOS) dataset. We obtained median extent-detection errors of 5.5-7.3mm or 3-4 voxels in the superior or inferior position in a dataset with average anatomical clippings of 24.8-43.6mm for four partially missing organs in the given FOV.
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