ArticleInternational journal of computer assisted radiology and surgery2021
Generation of annotated multimodal ground truth datasets for abdominal medical image registration.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- Phantom-based synthetic renal PCASL data generation for overcoming data scarcity and enhancing deep learning-based segmentation.Magma (New York, N.Y.) · 2026Article
- Unsupervised segmentation of dynamic pulmonary MRI using cross-modality adaptation with annotated CT images.International journal of computer assisted radiology and surgery · 2026Article
- Generation of multimodal realistic computational phantoms as a test-bed for validating deep learning-based cross-modality synthesis techniques.Medical & biological engineering & computing · 2026Article
- UAV-TIRVis: A Benchmark Dataset for Thermal-Visible Image Registration from Aerial Platforms.Journal of imaging · 2025Article
- A groupwise multiresolution network for DCE-MRI image registration.Scientific reports · 2025Article
- US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept.Journal of imaging informatics in medicine · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability.Zeitschrift fur medizinische Physik · 2024Article
- Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?Zeitschrift fur medizinische Physik · 2022Article
- Investigation of biases in convolutional neural networks for semantic segmentation using performance sensitivity analysis.Zeitschrift fur medizinische Physik · 2022Article
- Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization.Diagnostics (Basel, Switzerland) · 2022Article
- Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeSparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets.
methodsWe use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed.
resultsCompared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation.
conclusionOur proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.
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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.