Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation with hierarchical loss function, which enable to improve multilabel segmentation quality. Also we propose postprocessing technique to further increase labeling quality. Our model achieved new state-of-the-art 98.2% label accuracy on public RibSeg v2 dataset, surpassing previous result by 6.7%.

翻译：自动化的肋骨分割与编号能够提升计算机断层扫描评估效率并减少放射科医师的误判。本文提出一种采用层次化损失函数的多标签肋骨分割模型，该模型能够有效提升多标签分割质量。同时，我们提出一种后处理技术以进一步提高标注精度。我们的模型在公开数据集RibSeg v2上取得了98.2%的标签准确率，刷新了当前最优性能，较先前结果提升了6.7%。