Cargando…
Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net
MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tum...
Autores principales: | Ullah, Faizad, Ansari, Shahab U., Hanif, Muhammad, Ayari, Mohamed Arselene, Chowdhury, Muhammad Enamul Hoque, Khandakar, Amith Abdullah, Khan, Muhammad Salman |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624231/ https://www.ncbi.nlm.nih.gov/pubmed/34833602 http://dx.doi.org/10.3390/s21227528 |
Ejemplares similares
-
IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques
por: Ahamed, Md. Faysal, et al.
Publicado: (2023) -
Deep Learning Framework for Liver Segmentation from T(1)-Weighted MRI Images
por: Hossain, Md. Sakib Abrar, et al.
Publicado: (2023) -
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms
por: Ibtehaz, Nabil, et al.
Publicado: (2022) -
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
por: Mahmud, Sakib, et al.
Publicado: (2022) -
Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals
por: Shuzan, Md Nazmul Islam, et al.
Publicado: (2023)