  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%.
