AIMC Topic: Image Processing, Computer-Assisted

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A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network.

Artificial intelligence in medicine
This study proposes a deep convolutional neural network for the automatic segmentation of glioblastoma brain tumors, aiming sat replacing the manual segmentation method that is both time-consuming and labor-intensive. There are many challenges for au...

CT image denoising methods for image quality improvement and radiation dose reduction.

Journal of applied clinical medical physics
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in lo...

Cellular nucleus image-based smarter microscope system for single cell analysis.

Biosensors & bioelectronics
Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput ...

A deep learning-based interactive medical image segmentation framework with sequential memory.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Image segmentation is an essential component in medical image analysis. The case of 3D images such as MRI is particularly challenging and time consuming. Interactive or semi-automatic methods are thus highly desirable. Howev...

Medical image synthesis via conditional GANs: Application to segmenting brain tumours.

Computers in biology and medicine
Accurate brain tumour segmentation is critical for tasks such as surgical planning, diagnosis, and analysis, with magnetic resonance imaging (MRI) being the preferred modality due to its excellent visualisation of brain tissues. However, the wide int...

High-angular resolution diffusion imaging generation using 3d u-net.

Neuroradiology
PURPOSE: To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI).

Image Analysis-Based Machine Learning for the Diagnosis of Retinopathy of Prematurity: A Meta-analysis and Systematic Review.

Ophthalmology. Retina
TOPIC: To evaluate the performance of machine learning (ML) in the diagnosis of retinopathy of prematurity (ROP) and to assess whether it can be an effective automated diagnostic tool for clinical applications.

Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Computers in biology and medicine
Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-res...

A CNN-based model to count the leaves of rosette plants (LC-Net).

Scientific reports
Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major c...

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.

The Journal of investigative dermatology
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clini...