AIMC Topic: Image Processing, Computer-Assisted

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Image-based laparoscopic tool detection and tracking using convolutional neural networks: a review of the literature.

Computer assisted surgery (Abingdon, England)
Intraoperative detection and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware, such as tracking systems or the robot encoders, are cumbersome and lack accuracy, surgica...

Impact of Confounding Thoracic Tubes and Pleural Dehiscence Extent on Artificial Intelligence Pneumothorax Detection in Chest Radiographs.

Investigative radiology
OBJECTIVES: We hypothesized that published performances of algorithms for artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXRs) do not sufficiently consider the influence of PTX size and confounding effects caused by t...

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.

Journal of digital imaging
Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an...

Edge detection algorithm of cancer image based on deep learning.

Bioengineered
For the existing medical image edge detection algorithm image reconstruction accuracy is not high, the fitness of optimization coefficient is low, resulting in the detection results of low information recall, poor smoothness and low detection accurac...

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...

Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.

Applied optics
Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical ...

Deep learning applications to combat the dissemination of COVID-19 disease: a review.

European review for medical and pharmacological sciences
Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. ...

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

JCO clinical cancer informatics
Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of sta...