AIMC Topic:
Magnetic Resonance Imaging
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Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.
Academic radiology
Mar 7, 2024
BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this ci...
Brain MR image simulation for deep learning based medical image analysis networks.
Computer methods and programs in biomedicine
Mar 7, 2024
BACKGROUND AND OBJECTIVE: As large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the genera...
Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.
BMC cancer
Mar 7, 2024
PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed...
Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.
Scientific reports
Mar 7, 2024
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development o...
Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.
AJNR. American journal of neuroradiology
Mar 7, 2024
BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthe...
A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping.
IEEE journal of biomedical and health informatics
Mar 6, 2024
Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation....
Brain MR Image Classification Using Superpixel-Based Deep Transfer Learning.
IEEE journal of biomedical and health informatics
Mar 6, 2024
Nowadays, brain MR (Magnetic Resonance) images are widely used by clinicians to examine the brain's anatomy to look into various pathological conditions like cerebrovascular incidents and neuro-degenerative diseases. Generally, these diseases can be ...
Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.
Magnetic resonance in medicine
Mar 5, 2024
PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.
Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
BMC medical imaging
Mar 5, 2024
BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentatio...
A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.
Scientific reports
Mar 5, 2024
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public ava...
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