2019
DOI: 10.1109/access.2019.2952608
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ARU-Net: Research and Application for Wrist Reference Bone Segmentation

Abstract: Due to the influence of the irregular shapes and the adjacent positions of the wrist reference bones, it is difficult for the expert to accurately estimate the mature indication of the wrist reference bones of the minor. How to precisely segment the reference bones of the minor is a challenge. For this problem, the ARU-Net for wrist reference bone segmentation is proposed. First, we extract the reference bone ROI by Faster R-CNN. Then, the pre-processed ROI is fed into ARU-Net for segmentation. On the basis of… Show more

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Cited by 3 publications
(4 citation statements)
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“…Most segmentation studies based on deep learning [ 7 , 28 , 29 , 30 ] have used the U-Net [ 31 ] architecture, which is the most popular algorithm for biomedical image segmentation. However, our proposed segmentation model was based on the Mask R-CNN [ 24 ], which is widely used for instance segmentation because most adult wrist bones overlap each other, especially the eight carpal bones.…”
Section: Methodsmentioning
confidence: 99%
“…Most segmentation studies based on deep learning [ 7 , 28 , 29 , 30 ] have used the U-Net [ 31 ] architecture, which is the most popular algorithm for biomedical image segmentation. However, our proposed segmentation model was based on the Mask R-CNN [ 24 ], which is widely used for instance segmentation because most adult wrist bones overlap each other, especially the eight carpal bones.…”
Section: Methodsmentioning
confidence: 99%
“…The comparison results indicate that the value of DSC of our algorithm on the PROMISE12 dataset is significantly higher than other popular algorithms, which means that the predicted segmentation result of our algorithm is closest to the real segmentation mask. [50] 89.00 Deep dense multi-path neural network [51] 89.01 Atlas registration and ensemble deep convolutional neural network [25] 91.00 HD-net [52] 91.35 nnU-Net [53] 91.61 BOWDA-Net [54] 92.54 CDA-Net (Proposed) 92.88…”
Section: Quantitative Comparison With State-of-the-art Algorithmsmentioning
confidence: 99%
“…At the same time, a single model may need more training data to achieve better results. Therefore, there are still many researchers who employ cascade methods in medical image segmentation [24][25][26]. Xie et al [24] proposed a cascaded SE-ResNeXT U-Net for kidney tumor segmentation.…”
Section: Introductionmentioning
confidence: 99%
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