AIMC Topic: Image Processing, Computer-Assisted

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Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs).

The Angle orthodontist
OBJECTIVES: To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from thi...

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.

Network (Bristol, England)
Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help i...

Artificial intelligence in the detection and classification of dental caries.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking.

[Small bowel video keyframe retrieval based on multi-modal contrastive learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Retrieving keyframes most relevant to text from small intestine videos with given labels can efficiently and accurately locate pathological regions. However, training directly on raw video data is extremely slow, while learning visual representations...

Detection of precancerous lesions in cervical images of perimenopausal women using U-net deep learning.

African journal of reproductive health
Due to physiological changes during the perimenopausal period, the morphology of cervical cells undergoes certain alterations. Accurate cell image segmentation and lesion identification are of great significance for the early detection of precancerou...

Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.

Radiation protection dosimetry
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...

Artificial intelligence model for the assessment of unstained live sperm morphology.

Reproduction & fertility
ABSTRACT: Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal s...

RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output error...

TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View.

Human brain mapping
Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions ar...

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI.

Human brain mapping
Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-...