Abstract
Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis. However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques. Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus. However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays. We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle). On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph. We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net. We then present a mask encoding mechanism that can force the network to learn the background features. Transfer learning is ultimately introduced to help the network extract sufficient features. We evaluate the proposed network by fourfold cross validation on 88 chest radiography images. The proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively.
Highlights
Organ and tissue segmentation is essential in medical image preprocessing systems
To Journal of Healthcare Engineering facilitate the localization of lesions in lung disease detection systems and the measurement of medical values, such as rib spacing, in automated report generation systems, the complete set of clavicles and ribs has to be segmented from chest radiographs
We have constructed a dataset of chest X-rays and proposed a network framework and compared our method with the existing semantic segmentation network. e experimental results prove that the proposed networks exceed the performance of other segmentation networks. e main contributions of this study are as follows: (1) A dataset of chest radiograph for segmenting the clavicles and ribs: the dataset consists of 88 cases, each of which contains corresponding mask images of clavicles, anterior ribs, and posterior ribs with a resolution of 1024 pixels × 1024 pixels
Summary
MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph. We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle). On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph. We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net. We present a mask encoding mechanism that can force the network to learn the background features. E proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively We evaluate the proposed network by fourfold cross validation on 88 chest radiography images. e proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively
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