Adaptive Multi-dimensional Weighted Network with Category-aware Contrastive Learning for Fine-grained Hand Bone Segmentation

July 2024 Highlights

July 2024 Highlights 1271 748 Journal of Biomedical and Health Informatics (JBHI)

IEEE BHI 2024 banner
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024
10 – 13 November 2024 Houston, TX USA

Adaptive Multi-dimensional Weighted Network with Category-aware Contrastive Learning for Fine-grained Hand Bone Segmentation

Adaptive Multi-dimensional Weighted Network with Category-aware Contrastive Learning for Fine-grained Hand Bone Segmentation
Zeng, Bolun; Chen, Li; Zheng, Yuanyi; Chen, Xiaojun

Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insignificant feature differences among numerous bones. In this study, we have proposed a novel deep learning-based solution for pediatric hand bone segmentation in the US. Our method is unique in that it allows for effective detailed feature mining through an adaptive multi-dimensional weighting attention mechanism. It innovatively implements a category-aware contrastive learning method to highlight inter-class semantic feature differences, thereby enhancing the category discrimination performance of the model. Extensive experiments on the challenging pediatric clinical hand 3D US datasets show the outstanding performance of the proposed method in segmenting thirty-eight bone structures, with the average Dice coefficient of 90.0%. The results outperform other state-of-the-art methods, demonstrating its effectiveness in fine-grained hand bone segmentation. Our method will be globally released as a plugin in the 3D Slicer, providing an innovative and reliable tool for relevant clinical applications.

Sensor Informatics

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Decomposing Task-Relevant Information from Surface Electromyogram for User-Generic Dexterous
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GaitNet+ARL: A Deep Learning Algorithm for Interpretable Gait Analysis of Chronic Ankle Instability
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Predicting Arterial Stiffness from Single-Channel Photoplethysmography Signal: A Feature Interaction-Based Approach
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A Deep Learning Approach to Estimate Multi-Level Mental Stress from EEG using Serious Games
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Imaging Informatics

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Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis
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Predicting Alzheimer’s Disease Progression using a Versatile Sequence-Length-Adaptive Encoder-Decoder
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Medical Informatics

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Bioinformatics

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Superimposed Semantic Communication for IoT-based Real-time ECG Monitoring
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