# Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation

arXiv: https://arxiv.org/abs/2209.08355 · HTML: https://mlsanity.com/paper/2209.08355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Deep Distance Transform for Tubular Structure Segmentation in CT Scans](https://mlsanity.com/paper/1912.03383/) (2019)
- [Shape-Aware Organ Segmentation by Predicting Signed Distance Maps](https://mlsanity.com/paper/1912.03849/) (2019)
- [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)
- [A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs](https://mlsanity.com/paper/1908.08588/) (2019)
- [Automatic Airway Segmentation in chest CT using Convolutional Neural Networks](https://mlsanity.com/paper/1808.04576/) (2018)
- [BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding](https://mlsanity.com/paper/2202.00002/) (2022)
- [Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation](https://mlsanity.com/paper/1908.05099/) (2019)
- [Learning Surrogates via Deep Embedding](https://mlsanity.com/paper/2007.00799/) (2020)
- [LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation](https://mlsanity.com/paper/2202.06260/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision](https://mlsanity.com/paper/2303.09212/) (2023)

## Abstract (arXiv)

Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image analysis but have been performing poorly for cases when existing a significant imbalanced feature distribution, which is true for the airway data as the trachea and principal bronchi dominate most of the voxels whereas the lobar bronchi and distal segmental bronchi occupy a small proportion. In this paper, we propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to balance the training progress within-class distribution. Furthermore, a Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with superior sensitivity and minimize the variation of the distance map between the predictionand ground-truth. The proposed method is validated with the publically available reference airway segmentation datasets. The detected rate of branch and length on public EXACT'09 and BAS datasets are 82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the reliability and efficiency of the method in terms of improving the topology completeness of the segmentation performance while maintaining the overall topology accuracy.