AIMC Journal:
Medical image analysis

Showing 391 to 400 of 684 articles

Deep learning-based solvability of underdetermined inverse problems in medical imaging.

Medical image analysis Jan 16, 2021
Recently, with the significant developments in deep learning techniques, solving underdetermined inverse problems has become one of the major concerns in the medical imaging domain, where underdetermined problems are motivated by the willingness to p...
Tomography, X-Ray Computed Deep Learning Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Recent advances in medical image processing for the evaluation of chronic kidney disease.

Medical image analysis Jan 9, 2021
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (C...
Machine Learning Humans Magnetic Resonance Imaging Renal Insufficiency, Chronic Image Processing, Computer-Assisted Artificial Intelligence
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Angle-closure assessment in anterior segment OCT images via deep learning.

Medical image analysis Jan 7, 2021
Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ...
Anterior Eye Segment Glaucoma, Angle-Closure Iris Gonioscopy Humans Tomography, Optical Coherence Deep Learning
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Automated ultrasound assessment of amniotic fluid index using deep learning.

Medical image analysis Jan 7, 2021
The estimation of antenatal amniotic fluid (AF) volume (AFV) is important as it offers crucial information about fetal development, fetal well-being, and perinatal prognosis. However, AFV measurement is cumbersome and patient specific. Moreover, it i...
Humans Pregnancy Ultrasonography Amniotic Fluid Deep Learning Female
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Image registration: Maximum likelihood, minimum entropy and deep learning.

Medical image analysis Dec 18, 2020
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint...
Algorithms Deep Learning Entropy Humans Image Interpretation, Computer-Assisted Imaging, Three-Dimensional
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ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Medical image analysis Dec 17, 2020
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed cli...
Prostatic Neoplasms Magnetic Resonance Imaging Male Deep Learning Algorithms Humans
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High-resolution 3D abdominal segmentation with random patch network fusion.

Medical image analysis Dec 16, 2020
Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed tomography (CT) is a challenging topic, in part due to the limited memory provide by graphics processing units (GPU) and large number of parameters and ...
Imaging, Three-Dimensional Neural Networks, Computer Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Abdomen
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Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Medical image analysis Dec 16, 2020
Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuat...
Deep Learning Tomography, X-Ray Computed Humans Colon, Sigmoid Image Processing, Computer-Assisted
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Semi-supervised task-driven data augmentation for medical image segmentation.

Medical image analysis Dec 9, 2020
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of...
Male Humans Supervised Machine Learning Prostate
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Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.

Medical image analysis Dec 9, 2020
High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access to and adoption of this technique. As a potential solution to these challenges, we present a deep l...
Histological Techniques Neural Networks, Computer Magnetic Resonance Imaging Humans
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