4.7 Article

Deep learning with multiresolution handcrafted features for brain MRI segmentation

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

ResAttenGAN: Simultaneous segmentation of multiple spinal structures on axial lumbar MRI image using residual attention and adversarial learning

Hao Gong et al.

Summary: The paper introduces a novel network framework ResAttenGAN for simultaneous and accurate segmentation of multiple spinal structures, outperforming existing segmentation methods. This is achieved through three integrated modules: full feature fusion, residual refinement attention, and adversarial learning. ResAttenGAN addresses the challenges of diverse spinal structures and overfitting problems, resulting in improved performance in segmentation tasks.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2022)

Article Computer Science, Artificial Intelligence

Brain gray matter nuclei segmentation on quantitative susceptibility mapping using dual-branch convolutional neural network

Chao Chai et al.

Summary: The paper proposes a dual-branch residual-structured U-Net based on 3D convolutional neural network (CNN) for automatic segmentation of brain gray matter nuclei. By using QSM and 3D T-1 weighted imaging as inputs, the proposed method achieves better segmentation accuracy compared to conventional methods.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2022)

Article Engineering, Biomedical

Improving geometric P-norm-based glioma segmentation through deep convolutional autoencoder encapsulation

Wiem Takrouni et al.

Summary: In this paper, a PNorm-based glioma segmentation method encapsulated within a Deep Convolutional Autoencoder was proposed. Experimental results demonstrated the high-quality segmentation performance in multimodal MRI images and the potential for routine clinical practice.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Computer Science, Artificial Intelligence

Brain tumor segmentation based on the dual-path network of multi-modal MRI images

Lingling Fang et al.

Summary: This paper proposes a dual-path network based on multi-modal feature fusion to address the issue of tumor segmentation. The network effectively combines different kernel methods, reduces overlap frequency and vanishing gradient, and establishes a dual-path model to enhance segmentation accuracy.

PATTERN RECOGNITION (2022)

Article Biology

Dense Dilated Deep Multiscale Supervised U-Network for biomedical image segmentation

Shirsha Bose et al.

Summary: Biomedical image segmentation is crucial for medical image analysis, and deep learning algorithms allow for the design of advanced models to solve segmentation problems. The D3MSU-Net is proposed, which varies the receptive field at each level based on the resolution layer's depth and performs supervision at each resolution level. Experimental results demonstrate the superiority of the proposed network.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Artificial Intelligence

Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation

Imene Mecheter et al.

Summary: This study proposes an excitation-based CNN for bone segmentation in brain MR images by adaptively recalibrating network features. The method combines spatial squeeze and channel excitation block with channel squeeze and spatial excitation block, achieving improvements in segmentation performance and model complexity reduction.

NEUROCOMPUTING (2022)

Review Computer Science, Artificial Intelligence

Deep semantic segmentation of natural and medical images: a review

Saeid Asgari Taghanaki et al.

Summary: This review categorizes leading deep learning-based medical and non-medical image segmentation solutions into six main groups and provides a comprehensive review of each group's contributions. It analyzes the limitations of current approaches and presents potential future research directions for improving semantic image segmentation.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

Article Computer Science, Artificial Intelligence

Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks

Manjit Kaur et al.

Summary: The proposed approach utilizes NSCT and Xception for feature extraction and fusion of multi-modality medical images, outperforming competitive approaches in experimental results.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

Article Computer Science, Artificial Intelligence

A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images

Min Jiang et al.

Summary: The study introduces a novel CNN architecture DDU-net for automatic brain tumor segmentation on MR images. By optimizing edge-based information and introducing a regularization loss function, the segmentation performance is significantly improved to achieve ideal results.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Engineering, Biomedical

EMG hand gesture classification using handcrafted and deep features

Jose Manuel Fajardo et al.

Summary: The study introduces a method combining handcrafted features and deep features for electromyographic (EMG) signal gesture recognition. Experimental results showed an average classification accuracy of 81.54%, 88.54%, and 94.19% for 8, 6, and 5 gesture-classes, respectively.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Computer Science, Artificial Intelligence

Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

Yunyun Yang et al.

Summary: An accurate and automatic active contour model is proposed for tooth segmentation in CBCT images, utilizing deep learning techniques. By applying deep convolutional neural networks, designing shape prior information, and defining prior constraint terms, the model is able to accurately segment tooth images. Experimental results demonstrate the effectiveness and superior performance of the proposed model compared to other segmentation models.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Guilherme Freire Roberto et al.

Summary: This study proposes an ensemble model based on handcrafted fractal features and deep learning for histology image classification, achieving high accuracy and good performance on datasets with imbalanced classes. The model has fast training time and results are compatible with recent and relevant studies in the field.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Multi-scale attention U-net for segmenting clinical target volume in graves' ophthalmopathy

Junjie Hu et al.

Summary: Graves' ophthalmopathy (GO) is an autoimmune inflammatory disorder associated with thyroid disease, and radiotherapy is an effective treatment. A novel neural network architecture called MAU-Net is proposed to automatically segment the CTV more accurately for GO disease.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

A novel approach for computerized quantitative image analysis of proximal femur bone shape deformities based on the hip joint symmetry

Abbas Memis et al.

Summary: A novel approach was presented for the quantitative analysis of proximal femur shape deformities based on image data. By quantifying deformities in pathological proximal femurs in 2D and considering hip joint symmetry, the method showed promising results in representing the pathological shape deformities. The proposed method involved image preprocessing, automatic alignment, and quantification of mismatching areas to evaluate bone shape deformities in patients with Legg-Calve-Perthes disease.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Engineering, Biomedical

Multi-parametric MRI based radiomics with tumor subregion partitioning for differentiating benign and malignant soft-tissue tumors

Shengjie Shang et al.

Summary: This study utilized MRI-based radiomics approaches to distinguish malignant from benign soft-tissue tumors with handcrafted and deep learning-based features. The findings showed that the fusion radiomics nomogram had the best diagnostic performance, indicating the potential clinical value of tumoral and intratumoral radiomics in soft-tissue tumor diagnosis.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Geochemistry & Geophysics

SAR Speckle Removal Using Hybrid Frequency Modulations

Shuaiqi Liu et al.

Summary: This article introduces a hybrid denoising approach using CNN and CCS in the NSST domain to remove speckle noise in SAR images and retain more detailed information. The experimental results show that the method achieves better speckle removal performance compared to state-of-the-art algorithms.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Engineering, Biomedical

Multi scale decomposition based medical image fusion using convolutional neural network and sparse representation

D. Sunderlin Shibu et al.

Summary: The study introduces a novel medical image fusion system based on multi-scale decomposition, convolutional neural network, and sparse representation, achieving enhanced graphical quality of merged images in clinical analysis and treatment planning by combining frequency layers of different modal medical images.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Computer Science, Artificial Intelligence

Medical image fusion based on convolutional neural networks and non-subsampled contourlet transform

Zeyu Wang et al.

Summary: A novel multimodal medical image fusion method based on NSCT and CNN is proposed in this paper, which not only solves the problem of CNN inability to be directly used in medical image fusion, but also demonstrates its effectiveness in fusing multimodal medical images through subjective and objective evaluations.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Neurosciences

Automated claustrum segmentation in human brain MRI using deep learning

Hongwei Li et al.

Summary: In this study, a multi-view Deep Learning approach was used to segment the claustrum in T1-weighted MRI scans. The results showed good performance in humans and potential for assisting MRI-based research. The algorithm developed allows for robust automated claustrum segmentation and is publicly available for use.

HUMAN BRAIN MAPPING (2021)

Article Computer Science, Artificial Intelligence

Exploring deep features and ECG attributes to detect cardiac rhythm classes

Fatma Murat et al.

Summary: This study utilized a dataset with over 10,000 subject records to train a deep neural network (DNN) model for extracting ECG input features. The research also examined the impact of features obtained from DNN network on shallow classifiers.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Engineering, Chemical

A layered working condition perception integrating handcrafted with deep features for froth flotation

Xiaoliang Gao et al.

Summary: The method integrates handcrafted features and deep features to recognize the working condition in zinc flotation; a layered evaluation agency is established to determine if reidentification is needed, and deep features with support vector machine are applied for reidentification under specific working conditions.

MINERALS ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

Image scene geometry recognition using low-level features fusion at multi-layer deep CNN

Altaf Khan et al.

Summary: The proposed novel model of image scene geometry recognition integrates low-level handcrafted features with deep CNN multi-stage features using feature-fusion and score-level fusion strategies, resulting in improved recognition accuracy compared to existing models. By combining the advantages of both types of fusion, the proposed model outperforms other models in terms of recognition accuracy on different image datasets.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

C-CNN: Contourlet Convolutional Neural Networks

Mengkun Liu et al.

Summary: This article introduces a novel network architecture, C-CNN, combining spectral and spatial features for texture classification. Experimental results show that this approach outperforms other methods on multiple datasets with fewer trainable parameters.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Image classification with an RGB-channel nonsubsampled contourlet transform and a convolutional neural network

Lingling Fang et al.

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

Brain tumor segmentation with deep convolutional symmetric neural network

Hao Chen et al.

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

Iris anti-spoofing through score-level fusion of handcrafted and data-driven features

Meenakshi Choudhary et al.

APPLIED SOFT COMPUTING (2020)

Article Computer Science, Artificial Intelligence

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Jakub Nalepa et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

Article Geochemistry & Geophysics

Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network

Yuxing Zhao et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Computer Science, Artificial Intelligence

Hybrid channel based pedestrian detection

Fiseha B. Tesema et al.

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

Texture feed based convolutional neural network for pansharpening

Maryam Imani

NEUROCOMPUTING (2020)

Article Computer Science, Artificial Intelligence

Complex Contourlet-CNN for polarimetric SAR image classification

Lingling Li et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data

Hossein Arabi et al.

MEDICAL IMAGE ANALYSIS (2020)

Review Engineering, Biomedical

Attenuation correction for human PET/MRI studies

Ciprian Catana

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Computer Science, Artificial Intelligence

A deep attention-based ensemble network for real-time face hallucination

Dongdong Liu et al.

JOURNAL OF REAL-TIME IMAGE PROCESSING (2020)

Article Computer Science, Artificial Intelligence

A convolutional neural network with sparse representation

Guoan Yang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Review Psychology, Multidisciplinary

Effects of Neurological Disorders on Bone Health

Ryan R. Kelly et al.

FRONTIERS IN PSYCHOLOGY (2020)

Article Computer Science, Artificial Intelligence

Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning

Haithem Hermessi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI

Hossein Arabi et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

Article Computer Science, Interdisciplinary Applications

Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model

Ruichao Hou et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging

Hyungseok Jang et al.

MEDICAL PHYSICS (2018)

Article Computer Science, Artificial Intelligence

Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain

Haithem Hermessi et al.

NEURAL COMPUTING & APPLICATIONS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging

Fang Liu et al.

RADIOLOGY (2018)

Article Computer Science, Information Systems

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Abdullah-Al Nahid et al.

INFORMATION (2018)

Article Computer Science, Software Engineering

ShearLab 3D: Faithful Digital Shearlet Transforms Based on Compactly Supported Shearlets

Gitta Kutyniok et al.

ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE (2016)

Article Engineering, Electrical & Electronic

No-Reference Video Quality Assessment With 3D Shearlet Transform and Convolutional Neural Networks

Yuming Li et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Abolfazl Mehranian et al.

MEDICAL PHYSICS (2016)

Article Engineering, Multidisciplinary

Image fusion method using non-subsampled shearlet transform and fuzzy and simple fuzzy neural network algorithms

P. Subramanian et al.

JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Microscopic medical image classification framework via deep learning and shearlet transform

Hadi Rezaeilouyeh et al.

JOURNAL OF MEDICAL IMAGING (2016)

Article Computer Science, Interdisciplinary Applications

elastix: A Toolbox for Intensity-Based Medical Image Registration

Stefan Klein et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2010)

Article Computer Science, Artificial Intelligence

The nonsubsampled contourlet transform: Theory, design, and applications

Arthur L. da Cunha et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2006)