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Image Processing, Computer-Assisted - AI Medical Compendium

AIMC Topic: Image Processing, Computer-Assisted

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CA-XTree: Age Estimation of Grouped Gradient Regression Tree with Local Channel Attention.

Computational intelligence and neuroscience
Face age estimation has been widely used in video surveillance, human-computer interaction, market analysis, image processing analysis, and many fields. There are several problems that need to be solved in image-based face age estimation: (1) redunda...

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Medical image analysis
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic seg...

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm.

Physics in medicine and biology
The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially ...

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging.

Physics in medicine and biology
External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified b...

Optical flow estimation of coronary angiography sequences based on semi-supervised learning.

Computers in biology and medicine
Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce....

Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The successful integration of computer vision, robotic actuation, and photoacoustic imaging to find and follow targets of interest during surgical and interventional procedures requires accurate photoacoustic target detectability. This detectability ...

Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT.

BMC medical imaging
OBJECTIVE: We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT) images obtained during positron emission tomography/computed tomography (PET/CT) scans. The brain r...

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network.

Oxidative medicine and cellular longevity
Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apopto...

Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning.

Contrast media & molecular imaging
This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis super-resolution algorithm in the diagnosis of nonperitonealized colorectal cancer (CC). 103 patient...