# Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

## TL;DR

This paper introduces a novel deep learning approach that uses coordination-guided convolutional layers for accurate, automated segmentation of pulmonary lobes from chest CT images, aiding disease diagnosis.

## Contribution

It presents a new coordination-guided neural network architecture that improves lobar segmentation accuracy over existing methods.

## Key findings

- Achieved a mean Dice coefficient of 0.947, indicating high segmentation accuracy.
- Outperformed previous methods on publicly available datasets.
- Demonstrated the effectiveness of coordination-guided layers in medical image segmentation.

## Abstract

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.