Automated segmentation of subsolid pulmonary nodules in CT scans using deep learning
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2023-04-21
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en
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Abstract
Lung cancer is the second most diagnosed cancer and is the leading cause of cancer-related deaths
globally. A pulmonary nodule can turn into cancer, leading to fatal outcomes if left undetected. Compared to
other types of pulmonary nodules, subsolid nodules (SSN) pose a higher risk of malignancy. Subsolid nodules
can be categorized into two subtypes: ground-glass opacities (GGOs) or part-solid nodules (PSNs). The
assessment of SSNs by physicians on cancer risk and the stage is highly dependent on the size and volume
of the nodule. Therefore accurate segmentations are crucial for volumetric calculations when dealing with
SSNs. Currently, semi-automated methods are deployed to segment the boundaries of SSNs. This requires
a radiologist’s manual inputs and fine-tuning and could lead to sub-optimal results. Furthermore, there
is no study to date which focuses on evaluating the performance of deep learning in SSNs. Over the past
decade, deep learning has made significant strides in medical imaging segmentation, and networks like
nnUNet have demonstrated great potential in adapting to new datasets. In this research, nnUNet was used to
build a fully-automated segmentation model. However, the successful application of the model requires a
high-quality dataset with accurate annotations, particularly for the segmentation of SSNs, which has been an
area of limited research. To address this, our research focused on creating a carefully curated dataset with
annotations provided by an experienced radiologist using a dedicated lung screening workstation. The model
achieved a Dice similarity coefficient of 83.3% for the GGOs and 77.6% & 76% for non-solid and solid core
respectively for the PSNs on an external validation dataset. The model provides satisfactory segmentation
results in a minimal time, without any external input. It is able to learn the behaviour of the semi-automated
method to produce similar segmentation. The model has shown promising potential to generate accurate
and objective segmentation without human input for the subsolid nodules. The proposed model acts as a
good benchmark in the segmentation of subsolid pulmonary nodules.
Keywords: Pulmonary nodules, Subsolid nodules, nnUNet, lung screening
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Faculteit der Sociale Wetenschappen