HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
This addresses a clinically relevant issue for medical imaging by improving robustness in irregular anatomy cases, though it is incremental as it builds on existing segmentation methods.
The paper tackles the problem of organ hallucinations in deep learning-based medical image segmentation after organ resection surgery, proposing HALOS which combines missing organ classification with multi-organ segmentation to achieve higher Dice scores and near-zero false positive rates.
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.