Multi-Task Semi-Supervised Learning For Pulmonary Lobe Segmentation

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1329–1332

Abstract

Pulmonary lobe segmentation is an important preprocessing task for the analysis of lung diseases. Traditional methods relying on fissure detection or other anatomical features, such as the distribution of pulmonary vessels and airways, could provide reasonably accurate lobe segmentations. Deep learning based methods can outperform these traditional approaches, but require large datasets. Deep multi-task learning is expected to utilize labels of multiple different structures. However, commonly such labels are distributed over multiple datasets. In this paper, we proposed a multitask semi-supervised model that can leverage information of multiple structures from unannotated datasets and datasets annotated with different structures. A focused alternating training strategy is presented to balance the different tasks. We evaluated the trained model on an external independent CT dataset. The results show that our model significantly outperforms single-task alternatives, improving the mean surface distance from 7.174 mm to 4.196 mm. We also demonstrated that our approach is successful for different network architectures as backbones.

Authors 5

1. Jingnan Jia Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center (LUMC), P.O. Box 9600, Leiden, RC, The Netherlands

2. Zhiwei Zhai Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center (LUMC), P.O. Box 9600, Leiden, RC, The Netherlands

3. Irene Hernandez Giron Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center (LUMC), P.O. Box 9600, Leiden, RC, The Netherlands

4. Marius Staring Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center (LUMC), P.O. Box 9600, Leiden, RC, The Netherlands

5. Berend C. Stoel Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing, Leiden University Medical Center (LUMC), P.O. Box 9600, Leiden, RC, The Netherlands