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

arXiv: https://arxiv.org/abs/2406.16150 · HTML: https://mlsanity.com/paper/2406.16150/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2011.11952/) (2020)
- [AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks](https://mlsanity.com/paper/1907.06852/) (2019)
- [Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT](https://mlsanity.com/paper/2012.05767/) (2020)
- [Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation](https://mlsanity.com/paper/2209.02048/) (2022)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (2022)
- [Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis](https://mlsanity.com/paper/2207.00807/) (2022)
- [Recalibrating 3D ConvNets with Project & Excite](https://mlsanity.com/paper/2002.10994/) (2020)
- [Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing](https://mlsanity.com/paper/2102.12755/) (2021)
- [Adversarial Transformer for Repairing Human Airway Segmentation](https://mlsanity.com/paper/2210.12029/) (2022)

## Built on by (most cited first, 6 of 6)

- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models](https://mlsanity.com/paper/2408.05985/) (2024)
- [Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark](https://mlsanity.com/paper/2408.07444/) (2024)
- [AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans](https://mlsanity.com/paper/2607.28487/) (2026)
- [Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining](https://mlsanity.com/paper/2608.00231/) (2026)
- [Semantically Calibrated Evidence Composition for CT Vision-Language Learning](https://mlsanity.com/paper/2608.00239/) (2026)

## Abstract (arXiv)

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 textitIntensity 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.