Abstract
A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.
Highlights
It was clearly observed that our model correctly segmented this type of nodule
The proposed model correctly segmented the juxtavascular and well-circumscribed nodule shown in the third image of row 1, as well as the tiny nodule shown as the fourth image in row 1
The experimental setup was performed over the LIDC-IDRI dataset that comprises 986 sample nodular images
Summary
Statistical data reveal that lung cancer is an incurable disease with a worldwide survival rate of around 18% for only five years [1]. The nature of this disease requires diagnosis before time, and proper treatment planning is necessary for better treatment [2]. Due to the complexity of the disease, detection of cancer tends to be inaccurate, eventually affecting diagnosis and treatment planning. Computed tomography (CT) plays a vital part in the diagnosis as well as treatment of lung nodule cancer [6]
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