Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
By Johannes Hofmanninger, F. Prayer, Jeanny Pan, S. Röhrich, Helmut Prosch, Georg Langs. European Radiology Experimental, 2020
570 citations recorded by the source corpus.
Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited. We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets. Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024). The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/2446fefa0b4466592254ee90bad6737a511351ff
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In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- LViT: Language Meets Vision Transformer in Medical Image Segmentation (2022, methodology): “The first one is the MosMedData+1 dataset [11], [12], which contains 2729 CT scan slices of lung infections.”