Abstract
BackgroundAutomated 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.MethodsWe 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.ResultsUsing 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).ConclusionsThe 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.
Highlights
Automated segmentation of anatomical structures is a crucial step in image analysis
The translation of machine learning (ML) approaches developed on specific datasets to the variety of routine clinical data is of increasing importance
Automated lung segmentation algorithms are typically developed and tested on limited datasets, covering a limited variability by predominantly containing cases without severe pathology [4] or cases with a single class of disease [5]. Such specific cohort datasets are highly relevant in their respective domain but lead to specialised methods and ML models that struggle to generalise to unseen cohorts when utilised for the task of segmentation
Summary
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. The translation of machine learning (ML) approaches developed on specific datasets to the variety of routine clinical data is of increasing importance. Automated lung segmentation algorithms are typically developed and tested on limited datasets, covering a limited variability by predominantly containing cases without severe pathology [4] or cases with a single class of disease [5]. Such specific cohort datasets are highly relevant in their respective domain but lead to specialised methods and ML models that struggle to generalise to unseen cohorts when utilised for the task of segmentation. Disease-specific models are limited with respect to their applicability on undiagnosed cases such as in computer-aided diagnosis or diverse cross-sectional data
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