Make it more specific: A novel uncertainty based airway segmentation
  application on 3D U-Net and its variants
Shiyi Wang Yang Nan Felder Federico N Sheng Zhang Walsh Simon L F Guang Yang
Each medical segmentation task should be considered with a specific AI
algorithm based on its scenario so that the most accurate prediction model can
be obtained. The most popular algorithms in medical segmentation, 3D U-Net and
its variants, can directly implement the task of lung trachea segmentation, but
its failure to consider the special tree-like structure of the trachea suggests
that there is much room for improvement in its segmentation accuracy.
Therefore, a research gap exists because a great amount of state-of-the-art DL
algorithms are vanilla 3D U-Net structures, which do not introduce the various
performance-enhancing modules that come with special natural image modality in
lung airway segmentation. In this paper, we proposed two different network
structures Branch-Level U-Net (B-UNet) and Branch-Level CE-UNet (B-CE-UNet)
which are based on U-Net structure and compared the prediction results with the
same dataset. Specially, both of the two networks add branch loss and central
line loss to learn the feature of fine branch endings of the airways.
Uncertainty estimation algorithms are also included to attain confident
predictions and thereby, increase the overall trustworthiness of our whole
model. In addition, predictions of the lung trachea based on the maximum
connectivity rate were calculated and extracted during post-processing for
segmentation refinement and pruning.