Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation

TL;DR

This paper introduces an intensity-distance guided loss function to improve bronchus segmentation in CT images by addressing the challenge of intensity confusion between foreground and background voxels.

Contribution

It proposes a novel loss function that adaptively weights voxels based on intensity and distance priors to better handle intensity confusion in bronchus segmentation.

Findings

Outperforms state-of-the-art segmentation methods.

Effectively reduces errors caused by intensity confusion.

Significantly improves segmentation accuracy.

Abstract

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.