Cargando…

Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging

PURPOSE: Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation....

Descripción completa

Detalles Bibliográficos
Autores principales:	Gross, Moritz, Spektor, Michael, Jaffe, Ariel, Kucukkaya, Ahmet S., Iseke, Simon, Haider, Stefan P., Strazzabosco, Mario, Chapiro, Julius, Onofrey, John A.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2021
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635384/ https://www.ncbi.nlm.nih.gov/pubmed/34852007 http://dx.doi.org/10.1371/journal.pone.0260630

Ejemplares similares

LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis
por: Gross, Moritz, et al.
Publicado: (2023)

Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
por: Kucukkaya, Ahmet Said, et al.
Publicado: (2023)

Adherence to Tumor Board Recommendations in the Treatment of Patients with Hepatocellular Carcinoma
por: Cao, Yueming, et al.
Publicado: (2023)

Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation
por: Roy, Snehashis, et al.
Publicado: (2016)

Diabetes medications and risk of HCC
por: Plaz Torres, Maria Corina, et al.
Publicado: (2022)

Immune checkpoint inhibitors in malignancies after liver transplantation: A systematic review and pooled analysis
por: Kayali, Stefano, et al.
Publicado: (2022)

Segmentation of orbital and periorbital lesions detected in orbital magnetic resonance imaging by deep learning method
por: Aydin, Nevin, et al.
Publicado: (2022)

Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models
por: Kim, Yong-Woon, et al.
Publicado: (2021)

Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation
por: Chen, David, et al.
Publicado: (2022)

Somatodendritic consistency check for temporal feature segmentation
por: Asabuki, Toshitake, et al.
Publicado: (2020)

Revisiting Consistency for Semi-Supervised Semantic Segmentation †
por: Grubišić, Ivan, et al.
Publicado: (2023)

Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine
por: Perkuhn, Michael, et al.
Publicado: (2018)

Neural signatures of heterogeneity in risk‐taking and strategic consistency
por: Leota, Josh, et al.
Publicado: (2021)

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
por: Comelli, Albert, et al.
Publicado: (2021)

Pathophysiology of Cystic Fibrosis Liver Disease: A Channelopathy Leading to Alterations in Innate Immunity and in Microbiota
por: Fiorotto, Romina, et al.
Publicado: (2019)

Deep-Learning-Based Segmentation of Extraocular Muscles from Magnetic Resonance Images
por: Qureshi, Amad, et al.
Publicado: (2023)

Deep learning-based magnetic resonance image segmentation technique for application to glioma
por: Wan, Bing, et al.
Publicado: (2023)

The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries
por: Kaba, Şerife, et al.
Publicado: (2023)

Polyp segmentation with consistency training and continuous update of pseudo-label
por: Park, Hyun-Cheol, et al.
Publicado: (2022)

Modularity in the trilobite head consistent with the hypothesized segmental origin of the eyes
por: Vargas‐Parra, Ernesto E., et al.
Publicado: (2022)

Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy
por: Fransson, Samuel, et al.
Publicado: (2022)

Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images
por: An, Jeehye, et al.
Publicado: (2023)

Histological heterogeneity of glomerular segmental lesions in focal segmental glomerulosclerosis
por: Taneda, Sekiko, et al.
Publicado: (2011)

A New Hierarchy of Phylogenetic Models Consistent with Heterogeneous Substitution Rates
por: Woodhams, Michael D., et al.
Publicado: (2015)

Towards More Accurate and Complete Heterogeneous Iris Segmentation Using a Hybrid Deep Learning Approach
por: Meng, Yuan, et al.
Publicado: (2022)

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping
por: Ghadimi, Sona, et al.
Publicado: (2021)

DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images
por: Zeineldin, Ramy A., et al.
Publicado: (2020)

Wide and deep neural networks achieve consistency for classification
por: Radhakrishnan, Adityanarayanan, et al.
Publicado: (2023)

Bile acids and their receptors: modulators and therapeutic targets in liver inflammation
por: Bertolini, Anna, et al.
Publicado: (2022)

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning
por: Rodríguez Outeiral, Roque, et al.
Publicado: (2021)

Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning
por: Ito, Shota, et al.
Publicado: (2022)

Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images
por: Hu, Dingdu, et al.
Publicado: (2023)

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy
por: Rodríguez Outeiral, Roque, et al.
Publicado: (2023)

A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++
por: Wang, Jing, et al.
Publicado: (2023)

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs
por: Amjad, Asma, et al.
Publicado: (2023)

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning
por: Kalapos, András, et al.
Publicado: (2023)

Observation of J/ψp Resonances Consistent With Pentaquark States
por: Skwarnicki, Tomasz
Publicado: (2015)

Observation of $J/\psi p$ Resonances Consistent with Pentaquark States
por: Skwarnicki, Tomasz
Publicado: (2016)

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks
por: Pena, Rodrigo F. O., et al.
Publicado: (2018)

Dendrimeric nanosystem consistently circumvents heterogeneous drug response and resistance in pancreatic cancer
por: Liu, Juan, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_cc7m4p2nullmimmf1h6unsb8up