AIMC Topic: Image Processing, Computer-Assisted

Clear Filters Showing 6071 to 6080 of 10288 articles

Fast and efficient retinal blood vessel segmentation method based on deep learning network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated met...

Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.

Plant reproduction
Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answe...

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

Medical physics
PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and...

Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.

Journal of digital imaging
PURPOSE: The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, more precisely, detect small intestinal abnormalities like bleeding.

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves.

Sensors (Basel, Switzerland)
The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and mo...

Deep learning-based real-time detection of neurons in brain slices for in vitro physiology.

Scientific reports
A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist ...

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Physics in medicine and biology
Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been propos...

Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We investigate, by an extensive quality evaluation approach, performances and potential side effects introduced in Computed Tomography (CT) images by Deep Learning (DL) processing.

Automated severity scoring of atopic dermatitis patients by a deep neural network.

Scientific reports
Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has n...

Tumor attention networks: Better feature selection, better tumor segmentation.

Neural networks : the official journal of the International Neural Network Society
Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further ...