AIMC Topic: Image Processing, Computer-Assisted

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Neuroimage analysis using artificial intelligence approaches: a systematic review.

Medical & biological engineering & computing
In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. T...

Deep Learning for Automated Image Segmentation of the Middle Ear: A Scoping Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Convolutional neural networks (CNNs) have revolutionized medical image segmentation in recent years. This scoping review aimed to carry out a comprehensive review of the literature describing automated image segmentation of the middle ear ...

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.

Physics in medicine and biology
To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.The primary focus is on develo...

Automated facial landmark measurement using machine learning: A feasibility study.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: Information regarding facial landmark measurement using machine learning (ML) techniques in prosthodontics is lacking.

Optimized deep learning network for plant leaf disease segmentation and multi-classification using leaf images.

Network (Bristol, England)
Automatic detection of plant diseases is very imperative for monitoring the plants because they are one of the major concerns in the agricultural sector. Continuous monitoring can combat diseases of plants, which contribute to production loss. In the...

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning.

Medical & biological engineering & computing
The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel aut...

An effective segmentation and attention-based reptile residual capsule auto encoder for pest classification.

Pest management science
PURPOSE: Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection an...

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Medical image analysis
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with s...

Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

Computers in biology and medicine
This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentatio...

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

Journal of applied clinical medical physics
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...