1,405 results match your criteria u-net

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.

Nat Commun 2021 06 10;12(1):3541. Epub 2021 Jun 10.

Department of Pathology, Guangdong Provincial Women's and Children's Dispensary, Shenzhen, Guangdong Province, PR China.

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. Read More

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icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions.

Neuroimage Clin 2021 Jun 4;31:102707. Epub 2021 Jun 4.

icometrix, Leuven, Belgium.

Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease of the central nervous system. Its diagnosis nowadays commonly includes performing an MRI scan, as it is the most sensitive imaging test for MS. MS plaques are commonly identified from fluid-attenuated inversion recovery (FLAIR) images as hyperintense regions that are highly varying in terms of their shapes, sizes and locations, and are routinely classified in accordance to the McDonald criteria. Read More

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Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

Phys Med Biol 2021 Jun 9. Epub 2021 Jun 9.

Centrum Wiskunde en Informatica, Amsterdam, Noord-Holland, NETHERLANDS.

High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. Read More

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Technical Note: A Cascade 3D U-Net for Dose Prediction in Radiotherapy.

Med Phys 2021 Jun 8. Epub 2021 Jun 8.

Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education/School of Electrical Engineering and Automation, Anhui University, HeFei, 230601, China.

Purpose: Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep-learning models and advanced training strategies with a limited dataset to precisely predict three-dimensional (3D) dose distribution.

Methods: A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U-Net network units. Read More

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Fully Automated Segmentation of Brain Tumor from Multiparametric MRI Using 3D Context Deep Supervised U-Net.

Med Phys 2021 Jun 8. Epub 2021 Jun 8.

Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.

Purpose: Owing to histologic complexities of brain tumors, its diagnosis requires the use of multi-modalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice by slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning. Read More

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Using Neural Networks to Extend Cropped Medical Images for Deformable Registration Among Images with Differing Scan Extents.

Med Phys 2021 Jun 8. Epub 2021 Jun 8.

Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, 90024, USA.

Purpose: Missing or discrepant imaging volumes is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

Methods: Using a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. Read More

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Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

Diagn Interv Imaging 2021 Jun 4. Epub 2021 Jun 4.

Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France.

Purpose: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN).

Materials And Methods: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. Read More

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Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies.

BMC Med Imaging 2021 Jun 7;21(1):95. Epub 2021 Jun 7.

Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.

Background: Artificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measurement using a large dataset and investigated the clinical utility of the AI method.

Methods: Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI-only methods. Read More

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Touch-Point Detection Using Thermal Video With Applications to Prevent Indirect Virus Spread.

IEEE J Transl Eng Health Med 2021 24;9:4900711. Epub 2021 May 24.

Department of Mechanical Engineering and Materials ScienceDuke University Durham NC 27705 USA.

Viral and bacterial pathogens can be transmitted through direct contact with contaminated surfaces. Efficient decontamination of contaminated surfaces could lead to decreased disease transmission, if optimized methods for detecting contaminated surfaces can be developed. Here we describe such a method whereby thermal tracking technology is utilized to detect thermal signatures incurred by surfaces through direct contact. Read More

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A Feasibility Study on Deep Learning-Based Individualized 3D Dose Distribution Prediction.

Med Phys 2021 Jun 5. Epub 2021 Jun 5.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.

Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Read More

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Prenatal prediction and typing of placental invasion using MRI deep and radiomic features.

Biomed Eng Online 2021 Jun 5;20(1):56. Epub 2021 Jun 5.

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.

Background: To predict placental invasion (PI) and determine the subtype according to the degree of implantation, and to help physicians develop appropriate therapeutic measures, a prenatal prediction and typing of placental invasion method using MRI deep and radiomic features were proposed.

Methods: The placental tissue of abdominal magnetic resonance (MR) image was segmented to form the regions of interest (ROI) using U-net. The radiomic features were subsequently extracted from ROI. Read More

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Deformable adversarial registration network with multiple loss constraints.

Comput Med Imaging Graph 2021 May 26;91:101931. Epub 2021 May 26.

Video Processing and Communication Laboratory, Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.

Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation. Read More

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Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation.

Comput Biol Med 2021 May 15;134:104491. Epub 2021 May 15.

Center for Limb Loss and MoBility (CLiMB), VA Puget Sound, Seattle, WA, 98108, USA; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, WA, 98195, USA. Electronic address:

Histomorphological measurements can be used to identify microstructural changes related to disease pathomechanics, in particular, plantar soft tissue changes with diabetes. However, these measurements are time-consuming and susceptible to sampling and human measurement error. We investigated two approaches to automate segmentation of plantar soft tissue stained with modified Hart's stain for elastin with the eventual goal of subsequent morphological analysis. Read More

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DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images.

Int J Comput Assist Radiol Surg 2021 Jun 5. Epub 2021 Jun 5.

School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China.

Purpose: The global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing COVID-19 and treating patients, automatic COVID-19 lesion segmentation from computed tomography images helps doctors and patients intuitively understand lung infection. To effectively quantify lung infections, a convolutional neural network for automatic lung infection segmentation based on deep learning is proposed. Read More

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CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

J Nucl Cardiol 2021 Jun 3. Epub 2021 Jun 3.

Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Background: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. Read More

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3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework.

Phys Med Biol 2021 Jun 3. Epub 2021 Jun 3.

Department of Biomedical Engineering, Johns Hopkins University, School of Medicine, Baltimore, MD 21205, USA, Baltimore, 21205, UNITED STATES.

Purpose: Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient.

Methods: Ortho2D uses two independent Faster R-CNN networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. Read More

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Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

J Microsc 2021 Jun 3. Epub 2021 Jun 3.

Department of Computer Science, University of Tartu, Narva mnt 18, 51009, Estonia.

Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Read More

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A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images.

Comput Cardiol (2010) 2020 Sep 10;47. Epub 2021 Feb 10.

Chester F Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

In this work, we describe an unsupervised deep learning framework featuring a Laplacian-based operator as smoothing loss for deformable registration of 3D cine cardiac magnetic resonance (CMR) images. Before registration, the input 3D images are corrected for slice misalignment by segmenting the left ventricle (LV) blood-pool, LV myocardium and right ventricle (RV) blood-pool using a U-Net model and aligning the 2D slices along the center of the LV blood-pool. We conducted experiments using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Read More

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September 2020

Segmentation and Removal of Surgical Instruments for Background Scene Visualization from Endoscopic / Laparoscopic Video.

Proc SPIE Int Soc Opt Eng 2021 Feb 15;11598. Epub 2021 Feb 15.

Biomedical Modeling, Visualization and Image-guided Navigation (BiMVisIGN) Lab, RIT.

Surgical tool segmentation is becoming imperative to provide detailed information during intra-operative execution. These tools can obscure surgeons' dexterity control due to narrow working space and visual field-of-view, which increases the risk of complications resulting from tissue injuries (e.g. Read More

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February 2021

Joint Deep Learning Framework for Image Registration and Segmentation of Late Gadolinium Enhanced MRI and Cine Cardiac MRI.

Proc SPIE Int Soc Opt Eng 2021 Feb 15;11598. Epub 2021 Feb 15.

Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging, the current benchmark for assessment of myocardium viability, enables the identification and quantification of the compromised myocardial tissue regions, as they appear hyper-enhanced compared to the surrounding, healthy myocardium. However, in LGE CMR images, the reduced contrast between the left ventricle (LV) myocardium and LV blood-pool hampers the accurate delineation of the LV myocardium. On the other hand, the balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images ideal for accurate segmentation of the cardiac chambers. Read More

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February 2021

U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement.

SLAS Technol 2021 Jun 2:24726303211017532. Epub 2021 Jun 2.

Department of Biomedical Engineering, Konyang University, Daejeon, Korea.

Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2D cell-based assays by measuring initial seed cell numbers. These algorithms do not, however, quantitatively test in three-dimensional (3D) cell-based assays using extracellular matrix (ECM), because cells are aggregated and overlapped in the 3D structure of the ECM such as Matrigel, collagen, and alginate. Read More

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Automatic segmentation of the pharyngeal airway space with convolutional neural network.

J Dent 2021 May 30:103705. Epub 2021 May 30.

OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden.

Objectives: This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS).

Methods: A dataset of 103 computed tomography (CT) and cone-beam CT (CBCT) scans was acquired from an orthognathic surgery patients database. The acquisition devices consisted of 1 CT (128-slice multi-slice spiral CT, Siemens Somatom Definition Flash, Siemens AG, Erlangen, Germany) and 2 CBCT devices (Promax 3D Max, Planmeca, Helsinki, Finland and Newtom VGi evo, Cefla, Imola, Italy) with different scanning parameters. Read More

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Whole-body Composition Profiling Using a Deep Learning Algorithm: Influence of Different Acquisition Parameters on Algorithm Performance and Robustness.

Invest Radiol 2021 Jun 1. Epub 2021 Jun 1.

From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, and Faculty of Medicine, University of Zurich Computer Vision Lab, ETH Zurich, Zurich, Switzerland.

Objectives: To develop, test, and validate a body composition profiling algorithm for automated segmentation of body compartments in whole-body magnetic resonance imaging (wbMRI) and to investigate the influence of different acquisition parameters on performance and robustness.

Materials And Methods: A segmentation algorithm for subcutaneous and visceral adipose tissue (SCAT and VAT) and total muscle mass (TMM) was designed using a deep learning U-net architecture convolutional neuronal network. Twenty clinical wbMRI scans were manually segmented and used as training, validation, and test datasets. Read More

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Orchard Mapping with Deep Learning Semantic Segmentation.

Sensors (Basel) 2021 May 31;21(11). Epub 2021 May 31.

Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology-Hellas (CERTH), GR57001 Thessaloniki, Greece.

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e. Read More

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Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making.

Cancers (Basel) 2021 May 31;13(11). Epub 2021 May 31.

Department of Radiology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany.

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC).

Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (-Net architecture). Read More

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Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network.

Sensors (Basel) 2021 May 23;21(11). Epub 2021 May 23.

MB Prorega, 48212 Kaunas, Lithuania.

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Read More

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Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning.

Diagnostics (Basel) 2021 May 17;11(5). Epub 2021 May 17.

Technology Innovation and Engineering Education (TIEE), College of Engineering, Qatar University, Doha 2713, Qatar.

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder-Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. Read More

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Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation.

Sensors (Basel) 2021 May 7;21(9). Epub 2021 May 7.

School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Read More

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A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices.

Sensors (Basel) 2021 May 8;21(9). Epub 2021 May 8.

State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. Read More

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Comparison of convolutional neural network training strategies for cone-beam CT image segmentation.

Comput Methods Programs Biomed 2021 May 20;207:106192. Epub 2021 May 20.

Centrum Wiskunde & Informatica (CWI), Amsterdam 1090 GB, the Netherlands.

Background And Objective: Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Read More

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