AIMC Topic: Image Processing, Computer-Assisted

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Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Optics express
Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky mic...

Artificial Intelligence Models for Heart Chambers Segmentation from 2D Echocardiographic Images: A Scoping Review.

Studies in health technology and informatics
Echocardiography (echo) is a non-invasive, safe, widely available imaging modality that is frequently used to assess the heart structure and function. Accurate heart chamber segmentation is an essential step to quantify certain parameters, including ...

Artificial neural networks and computer vision in medicine and surgery.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti
Introduction: Artificial neural networks are becoming an essential technology in data analysis, and their influence is starting to permeate the field of medicine. Experimental surgery has been a long-term subject of study of our lab; this is naturall...

Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification.

The journal of medical investigation : JMI
Lung cancer is one of the life taking disease and causes more deaths worldwide. Early detection and treatment is necessary to save life. It is very difficult for doctors to interpret and identify diseases using imaging modalities alone. Therefore com...

Low-dose CT noise reduction based on local total variation and improved wavelet residual CNN.

Journal of X-ray science and technology
BACKGROUND: Low-dose computed tomography (LDCT) is an effective method for reducing radiation exposure. However, reducing radiation dose leads to considerable noise in the reconstructed image that can affect doctor's judgment.

Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst.

Methods in molecular biology (Clifton, N.J.)
Cell images provide a multitude of phenotypic information, which in its entirety the human eye can hardly perceive. Automated image analysis and machine learning approaches enable the unbiased identification and analysis of cellular mechanisms and as...

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

Methods in molecular biology (Clifton, N.J.)
The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability ...

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.

Journal of X-ray science and technology
OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).