Edge feature and detail-aware network integrated YOLOv8s algorithm for hip joint keypoint detection

Lv Jia; Duan Xunlu; Chen Xin

doi:10.12086/oee.2025.240281

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Lv J, Duan X L, Chen X. Edge feature and detail-aware network integrated YOLOv8s algorithm for hip joint keypoint detection[J]. Opto-Electron Eng, 2025, 52(3): 240281. doi: 10.12086/oee.2025.240281

Citation:

Lv J, Duan X L, Chen X. Edge feature and detail-aware network integrated YOLOv8s algorithm for hip joint keypoint detection[J]. Opto-Electron Eng, 2025, 52(3): 240281. doi: 10.12086/oee.2025.240281

Edge feature and detail-aware network integrated YOLOv8s algorithm for hip joint keypoint detection

1.
College of Computer and Information Sciences, Chongqing Normal University, Chongqing 401331, China
2.
National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
3.
Department of Radiology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China

Fund Project: National Natural Science Foundation Projects (11991024), Chongqing Education Commission Key Project (KJZD-K202200511), and Chongqing Natural Science Foundation Innovation and Development Joint Fund Key Project(CSTB2024NSCQ-LZX0090)

More Information

^*Corresponding author: b2309@126.com
CSTR: 32245.14.oee.2025.240281

Received Date 02 December 2024

Revised Date 24 February 2025

Accepted Date 25 February 2025

Published Date 28 March 2025

Abstract

Abstract

The accurate identification of the hip joint keypoint is vital for diagnosing developmental dysplasia of the hip. However, in pediatric hip X-ray images, bone regions around key points often exhibit low contrast and blurred edges, resulting in unclear edge features. Furthermore, down-sampling operations during feature extraction further weaken edge information. Key structures surrounding the keypoint are highly susceptible to background interference. Such factors hinder the precise localization of key points. An edge feature and detail-aware integrated YOLOv8s algorithm was proposed for hip joint key point detection. The algorithm designs an edge feature enhancement module to capture spatial information around key points and strengthen edge features. A detail-aware network was designed to integrate and refine multi-level features, enhancing image perception of fine structures. Experiments used a hip X-ray dataset from the Department of Radiology, Children's Hospital of Chongqing Medical University. Results showed reductions in average keypoint localization and angular errors to 4.2090 pixel and 1.4872°, respectively. These reductions, which are 6.8% and 9.9% compared to those of YOLOv8s, highlight significant improvements in detection accuracy. The algorithm enhances keypoint detection precision and provides valuable support for clinical diagnosis.
- developmental dysplasia of the hip /
- keypoint detection /
- YOLOv8s /
- edge feature enhancement /
- detail-aware network

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References

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Overview

Overview

Hip dysplasia is a common orthopedic disease in newborns, and timely and precise diagnosis is critical for optimal patient outcomes. In clinical diagnosis, specific key points on hip joint X-ray images are typically annotated, followed by the calculation of the acetabular index through angular measurements based on these points using goniometric tools. The diagnosis is then determined in combination with the age of the patient. However, manual annotation of key points in the hip joint not only demands that clinicians possess robust professional expertise and extensive clinical experience but also renders the process highly time-consuming and susceptible to subjective bias. Therefore, there is an urgent need for precise and automated key points detection technology to assist doctors in diagnosis. However, traditional template matching methods exhibit poor robustness and generalization when processing complex hip X-ray images, especially when faced challenges such as illumination changes, occlusions, and image rotations. To address these issues, researchers have enhanced the attention mechanism and extracted detailed information around key points using deep learning techniques, thereby improving the accuracy of key points localization. Nonetheless, these methods overlook the significance of bone edge information in assisting recognition and struggle with identifying local neighborhood key structural features, which limits further improvements in localization accuracy. To resolve these problems, an edge feature and detail-aware network integrated with the YOLOv8s algorithm for hip joint key points detection is proposed in this paper. This algorithm introduces an edge feature enhancement module to capture spatial features around key points and enhance the edge features of the bones where they are located. The module is applied multiple times during the feature extraction process of the network to progressively strengthen edge features and guide the network to focus on the key points edge areas. In addition, a detail-aware network is proposed to perform feature fusion and optimization on feature maps at different levels, enhancing the network's ability to capture important fine structures within the local neighborhood of key points. The algorithm was experimentally tested on the hip joint X-ray image dataset provided by the Department of Imaging of the Children's Hospital Affiliated to Chongqing Medical University. The results demonstrate that the average localization error and average angular error for key points have been reduced to 4.2090 pixel and 1.4872°, respectively, representing reductions of 6.8% and 9.9% compared with YOLOv8s, and significantly outperforms existing methods. The experimental findings confirm that the proposed algorithm effectively enhances the accuracy of key point detection, offering valuable insights for clinical diagnosis.