Lung, nodule, and airway segmentation using partially annotated data

Anne von Querfurth; Florian Kordon; Felix Denzinger; Katharina Breininger; Holger Kunze

doi:10.1117/12.2653419

3 April 2023 Lung, nodule, and airway segmentation using partially annotated data

Anne von Querfurth, Florian Kordon, Felix Denzinger, Katharina Breininger, Holger Kunze

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124661Q (2023) https://doi.org/10.1117/12.2653419
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

To support the development of an automatic path-planning procedure for bronchoscopy, semantic segmentation of pulmonary nodules and airways is required. The segmentation should happen simultaneously and automatically to save time and effort during the intervention. The challenges of the combined segmentation are the different shapes, frequencies, and sizes of airways, lungs, and pulmonary nodules. Therefore, a sampling strategy is explored using especially relevant crops of the volumes during training and weighting the classes differently, counteracting class imbalance. For the segmentation, a 3D U-Net is used. The proposed algorithm is compared to nnU-Net. First, it is trained as a one-class problem on all classes individually and in a second approach as a multi-label problem. The developed Multi-Label Segmentation network (MLS) is trained with full supervision. The results of the experiments have shown that without further adaption, a combined segmentation of nodules, airways, and lungs is complex. The multi-label nnU-Net failed to find nodules. Considering the different properties of the three classes, MLS accomplishes segmenting all classes simultaneously.

Citation Download Citation

Anne von Querfurth, Florian Kordon, Felix Denzinger, Katharina Breininger, and Holger Kunze "Lung, nodule, and airway segmentation using partially annotated data", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124661Q (3 April 2023); https://doi.org/10.1117/12.2653419

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