3D joints estimation of human body using part segmentation

Xu, Tianxu; An, Dong; Jia, Yuetong; Chen, Jiaqing; Zhong, Hongkun; Ji, Yishen; Wang, Yushi; Wang, Zhonghan; Wang, Qiang; Pan, Zhongqi; Yue, Yang

doi:10.1016/j.ins.2022.04.025

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Finally, we have validated the efficacy of enhanced data using a supervised segmentation model and an image of the synthetic knee with annotation at a resolution of 256 × 256. The knee image synthesis is a challenging task due to its complex structure and diverse anatomical geometry [37], [38]. The proposed framework has succeeded in producing realistic deepfake images through the use of spectral normalization technique.…”

Section: Discussionmentioning

confidence: 99%

DEEPFAKE Image Synthesis for Data Augmentation

et al. 2022

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Deepfake videos and images have increased in recent years at exponential rates, and refer to the transfer of features of interest from source image (or video) to target image (or video), such that the target modality appears to animate the source almost close to the real. The forensic professionals, policy makers, and the public alike are interested in the role of Deepfakes is playing to confuse viewers in spreading misinformation by undermining the truth. In the past decade, computer vision has made significant advances using the latest state-of-art-methods of image processing techniques. Supervised Deep learning models produce super-human results in a variety of computer vision and machine learning applications. The field of medical imaging is scarce in terms of a reliable data set that is extensive enough to train distinct models. One way to tackle this problem is to use a Generative Adversarial Network to synthesise DEEPFAKE images to augment the data. DEEPFAKE images can be a useful means in various applications like translating to different useful and sometimes malicious modalities, unbalanced datasets or increasing the amount of datasets. In this paper the data scarcity has been addressed by using Progressive Growing Generative Adversarial Networks (PGGAN). However, PGGAN consists of convolution layer that suffers from the training-related issues. PGGAN requires a large number of convolution layers in order to obtain highresolution image training, which makes training a difficult task. In this work, a subjective self-attention layer has been added with convolution layers for efficient feature learning and the use of spectral normalization for training stabilization -the two tasks resulting into what is referred to as Enhanced-GAN. The Enhanced-GAN performance is compared to PGGAN performance using the parameters of AM Score and Mode Score. In addition, the strength of Enhanced-GAN and PGGAN synthesis is evaluated using the U-net model for segmentation tasks. Dice Coefficient metrics show that U-net trained on Enhanced-GAN DEEPFAKE data optimized with real data performs better than PGGAN DEEPFAKE data with real data.

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Section: Discussionmentioning

confidence: 99%

DEEPFAKE Image Synthesis for Data Augmentation

et al. 2022

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A Proposal for Markerless Gait Analysis Based on 3D Points Cloud

da Rocha,

Rudek

2024

Lecture Notes in Networks and Systems

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An Automatic Method for Elbow Joint Recognition, Segmentation and Reconstruction

Cui,

Ji,

Zha

et al. 2024

Sensors

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Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.

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3D joints estimation of human body using part segmentation

Cited by 3 publications

References 28 publications

DEEPFAKE Image Synthesis for Data Augmentation

DEEPFAKE Image Synthesis for Data Augmentation

A Proposal for Markerless Gait Analysis Based on 3D Points Cloud

An Automatic Method for Elbow Joint Recognition, Segmentation and Reconstruction

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