PP.23.09

Li, L.H.; Cheng, Yi-Bang; Zhang, L.; Wei, Fang‐Fei; Kang, Yu-Hong; Wang, S.; Huang, Qi‐Fang; Sheng, Chunhong; Wang, J.G.; Li, Y.

doi:10.1097/01.hjh.0000468422.61399.c9

Cited by 2 publications

(2 citation statements)

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“…To evaluate the performance of different deep learning models in landmark detection, we cited the indexes from [13,39] and defined the point-to-point error between the reference standard and coordinates predicted by the model as PE.…”

Section: Evaluation Indexmentioning

confidence: 99%

Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative

Pei

et al. 2023

Biomed. Phys. Eng. Express

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Patients with developmental dysplasia of the hip can have this problem throughout their lifetime. The problem is difficult to detect by radiologists throughout X-ray because of an abrasion of anatomical structures. Thus, the landmarks should be automatically and precisely located. In this paper, we propose an attention mechanism of combining multi-dimension information on the basis of separating spatial dimension. The proposed attention mechanism decouples spatial dimension and forms width-channel dimension and height-channel dimension by 1D pooling operations in the height and width of spatial dimension. Then non-local means operations are performed to capture the correlation between long-range pixels in width-channel dimension, as well as that in height-channel dimension at different resolutions. The proposed attention mechanism modules are inserted into the skipped connections of U-Net to form a novel landmark detection structure. This landmark detection method was trained and evaluated through five-fold cross-validation on an open-source dataset, including 524 pelvis X-ray, each containing eight landmarks in pelvis, and achieved excellent performance compared to other landmark detection models. The average point-to-point errors of U-Net, HR-Net, CE-Net, and the proposed network were 3.5651 mm, 3.6118 mm, 3.3914 mm and 3.1350 mm, respectively. The results indicate that the proposed method has the highest detection accuracy. Furthermore, an open-source pelvis dataset is annotated and released for open research.

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Section: Evaluation Indexmentioning

confidence: 99%

Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative

Pei

et al. 2023

Biomed. Phys. Eng. Express

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“…Li Bicheng thinks the conflicting evidences should not be completely denied and K can be assigned to every focus element based on average support degree [6]. Thus, the combination rule can be defined as follows:…”

Section: Bicheng's Solution Approachmentioning

confidence: 99%

Information Fusion Based on Improved D-S Evidence Theory

Pan

2013

AMM

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Dempster-Shafer evidence theory is an efficient method to process uncertain, incomplete and vague information in data fusion. Aiming at the failure of conditional D-S evidence theory in dealing with conflicting evidences, an improved D-S combination rule is recommended. The proposed method utilizes the information quantity of evidence and assigns the conflict factor to every focus element based on average support degree. The simulations show that the proposed D-S combination rule can effectively solve thorny problems for conflicting evidences.

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PP.23.09

Cited by 2 publications

References 0 publications

Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative

Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative

Information Fusion Based on Improved D-S Evidence Theory

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