ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2020
Deep adaptive registration of multi-modal prostate images.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- ACSwinNet: A Deep Learning-Based Rigid Registration Method for Head-Neck CT-CBCT Images in Image-Guided Radiotherapy.Sensors (Basel, Switzerland) · 2024Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.Medical image analysis · 2023Article
- Cross-modal attention for multi-modal image registration.Medical image analysis · 2022Article
- Prevalence and outcomes of focal ablation versus prostatectomy for elderly patients with prostate cancer: a population-based study.Journal of the National Cancer Center · 2022Article
- A review of deep learning-based deformable medical image registration.Frontiers in oncology · 2022Review
- A review of deep learning-based three-dimensional medical image registration methods.Quantitative imaging in medicine and surgery · 2021Review
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
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Authors and funding
5 authors.
Funding
Abstract
Artificial intelligence, especially the deep learning paradigm, has posed a considerable impact on cancer imaging and interpretation. For instance, fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images to guide prostate cancer biopsy can significantly improve the diagnosis. However, multi-modal image registration is still challenging, even with the latest deep learning technology, as it requires large amounts of labeled transformations for network training. This paper aims to address this problem from two angles: (i) a new method of generating large amount of transformations following a targeted distribution to improve the network training and (ii) a coarse-to-fine multi-stage method to gradually map the distribution from source to target. We evaluate both innovations based on a multi-modal prostate image registration task, where a T2-weighted MR volume and a reconstructed 3D ultrasound volume are to be aligned. Our results demonstrate that the use of data generation can significantly reduce the registration error by up to 62%. Moreover, the multi-stage coarse-to-fine registration technique results in a mean surface registration error (SRE) of 3.66 mm (with the initial mean SRE of 9.42 mm), which is found to be significantly better than the one-step registration with a mean SRE of 4.08 mm.
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