ArticleScientific reports2021
A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- Development and evaluation of an automated report-based chart checking tool in external beam radiotherapy.Journal of applied clinical medical physics · 2026Article
- Multi-modal and inter-device retinal OCT registration via learning-based feature correspondence.Biomedical optics express · 2026Article
- A deep learning framework for the localization of landmarks on the lateral semi circular canals.PloS one · 2026Article
- A Review of Medical Image Registration for Different Modalities.Bioengineering (Basel, Switzerland) · 2024Review
- Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections.Nature communications · 2024Article
- A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA.iScience · 2024Article
- Multi-modal tumor segmentation methods based on deep learning: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease.Diagnostics (Basel, Switzerland) · 2023Review
- Temporal focusing multiphoton microscopy with cross-modality multi-stage 3D U-Net for fast and clear bioimaging.Biomedical optics express · 2023Article
- A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.Sensors (Basel, Switzerland) · 2022Article
- Is image-to-image translation the panacea for multimodal image registration? A comparative study.PloS one · 2022Article
- Projective Diffeomorphic Mapping of Molecular Digital Pathology with Tissue MRI.Communications engineering · 2022Article
- Combined Use of Modal Analysis and Machine Learning for Materials Classification.Materials (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.