Cargando…

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning

OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. METHODS: In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001)...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ryu, Hwaseong, Shin, Seung Yeon, Lee, Jae Young, Lee, Kyoung Mu, Kang, Hyo-jin, Yi, Jonghyon
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2021
Materias:	Imaging Informatics and Artificial Intelligence
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523410/ https://www.ncbi.nlm.nih.gov/pubmed/33881566 http://dx.doi.org/10.1007/s00330-021-07850-9

Ejemplares similares

Deep Learning–driven classification of external DICOM studies for PACS archiving
por: Jonske, Frederic, et al.
Publicado: (2022)

COVID-19 classification of X-ray images using deep neural networks
por: Keidar, Daphna, et al.
Publicado: (2021)

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning
por: Lin, Yu-Chun, et al.
Publicado: (2023)

Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network
por: Chen, Chaoyue, et al.
Publicado: (2022)

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time
por: Jing, Xueping, et al.
Publicado: (2022)

Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI
por: Rahimpour, Masoomeh, et al.
Publicado: (2022)

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
por: Cheng, Chi-Tung, et al.
Publicado: (2019)

Algorithmic transparency and interpretability measures improve radiologists’ performance in BI-RADS 4 classification
por: Jungmann, Friederike, et al.
Publicado: (2022)

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis
por: Xue, Li-Yun, et al.
Publicado: (2020)

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis
por: van Kempen, Evi J., et al.
Publicado: (2021)

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
por: Schmitt, N., et al.
Publicado: (2021)

Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation
por: Weikert, T., et al.
Publicado: (2023)

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network
por: Ye, Hai, et al.
Publicado: (2019)

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks
por: Koitka, Sven, et al.
Publicado: (2020)

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning
por: Duan, Caohui, et al.
Publicado: (2021)

Pediatric age estimation from radiographs of the knee using deep learning
por: Demircioğlu, Aydin, et al.
Publicado: (2022)

Deep learning in interstitial lung disease—how long until daily practice
por: Trusculescu, Ana Adriana, et al.
Publicado: (2020)

Deep learning to automate the labelling of head MRI datasets for computer vision applications
por: Wood, David A., et al.
Publicado: (2021)

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps
por: Wesp, Philipp, et al.
Publicado: (2022)

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach
por: Mobini, Nazanin, et al.
Publicado: (2023)

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning
por: Nowak, Sebastian, et al.
Publicado: (2021)

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model
por: Yin, Yunchao, et al.
Publicado: (2021)

Decision-making based on 3D printed models in laparoscopic liver resections with intraoperative ultrasound: a prospective observational study
por: Witowski, Jan, et al.
Publicado: (2019)

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
por: Wang, Shuai, et al.
Publicado: (2021)

Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema
por: Nowak, Sebastian, et al.
Publicado: (2022)

Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability
por: Opfer, Roland, et al.
Publicado: (2022)

Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network
por: Haubold, Johannes, et al.
Publicado: (2021)

Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning
por: Valmaggia, Philippe, et al.
Publicado: (2022)

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network
por: Dratsch, Thomas, et al.
Publicado: (2020)

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability
por: Netzer, Nils, et al.
Publicado: (2023)

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge
por: Hosseinzadeh, Matin, et al.
Publicado: (2021)

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
por: Hou, Min, et al.
Publicado: (2022)

Improvement of late gadolinium enhancement image quality using a deep learning–based reconstruction algorithm and its influence on myocardial scar quantification
por: van der Velde, Nikki, et al.
Publicado: (2020)

Deep learning assisted contrast-enhanced CT–based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases
por: Xu, Xiaoshuai, et al.
Publicado: (2022)

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study
por: Xie, Qiuchen, et al.
Publicado: (2020)

Deep Learning Segmentation, Visualization, and Automated 3D Assessment of Ciliary Body in 3D Ultrasound Biomicroscopy Images
por: Minhaz, Ahmed Tahseen, et al.
Publicado: (2022)

Deep Ensemble Learning for Retinal Image Classification
por: Ho, Edward, et al.
Publicado: (2022)

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists
por: Antonelli, Michela, et al.
Publicado: (2019)

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform
por: Fornacon-Wood, Isabella, et al.
Publicado: (2020)

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2
por: Fang, Xu, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_p705stjadpgvgl1thjc7hgqmo1