  The wide range of research in deep learning-based medical image segmentation
pushed the boundaries in a multitude of applications. A clinically relevant
problem that received less attention is the handling of scans with irregular
anatomy, e.g., after organ resection. State-of-the-art segmentation models
often lead to organ hallucinations, i.e., false-positive predictions of organs,
which cannot be alleviated by oversampling or post-processing. Motivated by the
increasing need to develop robust deep learning models, we propose HALOS for
abdominal organ segmentation in MR images that handles cases after organ
resection surgery. To this end, we combine missing organ classification and
multi-organ segmentation tasks into a multi-task model, yielding a
classification-assisted segmentation pipeline. The segmentation network learns
to incorporate knowledge about organ existence via feature fusion modules.
Extensive experiments on a small labeled test set and large-scale UK Biobank
data demonstrate the effectiveness of our approach in terms of higher
segmentation Dice scores and near-to-zero false positive prediction rate.
