AIMC Topic: Image Processing, Computer-Assisted

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Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as gener...

Application of Deep Learning Workflow for Autonomous Grain Size Analysis.

Molecules (Basel, Switzerland)
Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alt...

Comparative study of deep learning algorithms for atomic force microscopy image denoising.

Micron (Oxford, England : 1993)
Atomic force microscopy (AFM) enables direct visualisation of surface topography at the nanoscale. However, post-processing is generally required to obtain accurate, precise, and reliable AFM images owing to the presence of image artefacts. In this s...

Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.

European radiology
OBJECTIVES: To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) ...

Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.

Medical physics
PURPOSE: Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging diffe...

Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

Scientific reports
In this study, we propose a two-stage workflow used for the segmentation and scoring of lung diseases. The workflow inherits quantification, qualification, and visual assessment of lung diseases on X-ray images estimated by radiologists and clinician...

Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan.

Physics in medicine and biology
. Accurate segmentation of the pancreas from abdomen CT scans is highly desired for diagnosis and treatment follow-up of pancreatic diseases. However, the task is challenged by large anatomical variations, low soft-tissue contrast, and the difficulty...

Evaluation and comparison of smartphone application tracing, web based artificial intelligence tracing and conventional hand tracing methods.

Journal of stomatology, oral and maxillofacial surgery
AIM: The aim of this study was to compare and evaluate the reliability of three different cephalometric assessment methods: Smartphone Application Tracing Method CephNinja (SATM), Web Based Artificial Intelligence (AI) Driven Tracing Method WebCeph (...

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Sensors (Basel, Switzerland)
Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and suscept...

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

BioMed research international
A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image s...