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.

翻译：基于深度学习的医学图像分割研究在众多应用中不断突破边界。一个较少受到关注的临床相关问题是对异常解剖结构（如器官切除术后）扫描数据的处理。现有最先进的分割模型常导致器官幻觉，即器官的假阳性预测，而通过过采样或后处理无法缓解该问题。受开发鲁棒深度学习模型日益增长的需求驱动，我们提出HALOS方法，用于处理磁共振图像中器官切除术后的腹部器官分割。为此，我们将缺失器官分类与多器官分割任务结合为多任务模型，构建了一个分类辅助的分割流水线。分割网络通过特征融合模块学习融入关于器官存在的知识。在小规模标注测试集及大规模英国生物样本库数据上的广泛实验表明，本方法在提升分割Dice分数及实现近乎零假阳性预测率方面具有有效性。