  Automatic segmentation of the bronchial tree from CT imaging is important, as
it provides structural information for disease diagnosis. Despite the merits of
previous automatic bronchus segmentation methods, they have paied less
attention to the issue we term as \textit{Intensity Confusion}, wherein the
intensity values of certain background voxels approach those of the foreground
voxels within bronchi. Conversely, the intensity values of some foreground
voxels are nearly identical to those of background voxels. This proximity in
intensity values introduces significant challenges to neural network
methodologies. To address the issue, we introduce a novel Intensity-Distance
Guided loss function, which assigns adaptive weights to different image voxels
for mining hard samples that cause the intensity confusion. The proposed loss
estimates the voxel-level hardness of samples, on the basis of the following
intensity and distance priors. We regard a voxel as a hard sample if it is in:
(1) the background and has an intensity value close to the bronchus region; (2)
the bronchus region and is of higher intensity than most voxels inside the
bronchus; (3) the background region and at a short distance from the bronchus.
Extensive experiments not only show the superiority of our method compared with
the state-of-the-art methods, but also verify that tackling the intensity
confusion issue helps to significantly improve bronchus segmentation. Project
page: https://github.com/lhaof/ICM.
