# Automatic Pulmonary Lobe Segmentation Using Deep Learning

arXiv: https://arxiv.org/abs/1903.09879 · HTML: https://mlsanity.com/paper/1903.09879/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2019.

## Built on by (most cited first, 15 of 15)

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- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)
- [Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs](https://mlsanity.com/paper/2105.05204/) (2021)
- [To what extent can Plug-and-Play methods outperform neural networks alone in low-dose CT reconstruction](https://mlsanity.com/paper/2202.07173/) (2022)
- [Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks](https://mlsanity.com/paper/2303.01036/) (2023)
- [PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion](https://mlsanity.com/paper/2312.08323/) (2023)
- [MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis](https://mlsanity.com/paper/2507.15340/) (2025)

## Abstract (arXiv)

Pulmonary lobe segmentation is an important task for pulmonary disease related Computer Aided Diagnosis systems (CADs). Classical methods for lobe segmentation rely on successful detection of fissures and other anatomical information such as the location of blood vessels and airways. With the success of deep learning in recent years, Deep Convolutional Neural Network (DCNN) has been widely applied to analyze medical images like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which, however, requires a large number of ground truth annotations. In this work, we release our manually labeled 50 CT scans which are randomly chosen from the LUNA16 dataset and explore the use of deep learning on this task. We propose pre-processing CT image by cropping region that is covered by the convex hull of the lungs in order to mitigate the influence of noise from outside the lungs. Moreover, we design a hybrid loss function with dice loss to tackle extreme class imbalance issue and focal loss to force model to focus on voxels that are hard to be discriminated. To validate the robustness and performance of our proposed framework trained with a small number of training examples, we further tested our model on CT scans from an independent dataset. Experimental results show the robustness of the proposed approach, which consistently improves performance across different datasets by a maximum of 5.87% as compared to a baseline model.