Search Results for author: Haiqiao Wang

Found 5 papers, 4 papers with code

A Review of Image Processing Methods in Prostate Ultrasound

no code implementations30 Jun 2024 Haiqiao Wang, Hong Wu, Zhuoyuan Wang, Peiyan Yue, Dong Ni, Pheng-Ann Heng, Yi Wang

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates.

Image Registration

Encoding Matching Criteria for Cross-domain Deformable Image Registration

1 code implementation18 Jun 2024 Zhuoyuan Wang, Haiqiao Wang, Yi Wang

Most existing deep learning-based registration methods are trained on single-type images to address same-domain tasks. However, cross-domain deformable registration remains challenging. We argue that the tailor-made matching criteria in traditional registration methods is one of the main reason they are applicable in different domains. Motivated by this, we devise a registration-oriented encoder to model the matching criteria of image features and structural features, which is beneficial to boost registration accuracy and adaptability. Specifically, a general feature encoder (Encoder-G) is proposed to capture comprehensive medical image features, while a structural feature encoder (Encoder-S) is designed to encode the structural self-similarity into the global representation. Extensive experiments on images from three different domains prove the efficacy of the proposed method.

Image Registration One-Shot Learning

Pyramid Attention Network for Medical Image Registration

1 code implementation14 Feb 2024 Zhuoyuan Wang, Haiqiao Wang, Yi Wang

The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods. However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration. The pure convolutional neural networks (CNNs) neglect feature enhancement, while current Transformer-based networks are susceptible to information redundancy. To alleviate these issues, we propose a pyramid attention network (PAN) for deformable medical image registration. Specifically, the proposed PAN incorporates a dual-stream pyramid encoder with channel-wise attention to boost the feature representation. Moreover, a multi-head local attention Transformer is introduced as decoder to analyze motion patterns and generate deformation fields. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets and one abdominal MRI dataset demonstrate that our method achieves favorable registration performance, while outperforming several CNN-based and Transformer-based registration networks. Our code is publicly available at https://github. com/JuliusWang-7/PAN.

Decoder Image Registration +1

ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer

1 code implementation9 Jun 2023 Haiqiao Wang, Dong Ni, Yi Wang

The Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration.

Image Registration Medical Image Registration

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