Radiomics: a primer on high-throughput image phenotyping

KJ Lafata, Y Wang, B Konkel, FF Yin, MR Bashir - Abdominal Radiology, 2022 - Springer
Radiomics is a high-throughput approach to image phenotyping. It uses computer
algorithms to extract and analyze a large number of quantitative features from radiological …

Harmonization strategies in multicenter MRI-based radiomics

E Stamoulou, C Spanakis, GC Manikis, G Karanasiou… - Journal of …, 2022 - mdpi.com
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient
information directly from images that are decoded into handcrafted features, comprising …

Bridging the gap between 2D and 3D contexts in CT volume for liver and tumor segmentation

L Song, H Wang, ZJ Wang - IEEE journal of biomedical and …, 2021 - ieeexplore.ieee.org
Automatic liver and tumor segmentation remain a challenging topic, which subjects to the
exploration of 2D and 3D contexts in CT volume. Existing methods are either only focus on …

Focal dice loss-based V-Net for liver segments classification

B Prencipe, N Altini, GD Cascarano, A Brunetti… - Applied Sciences, 2022 - mdpi.com
Liver segmentation is a crucial step in surgical planning from computed tomography scans.
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …

A deep learning framework for automated detection and quantitative assessment of liver trauma

N Farzaneh, EB Stein, R Soroushmehr, J Gryak… - BMC Medical …, 2022 - Springer
Background Both early detection and severity assessment of liver trauma are critical for
optimal triage and management of trauma patients. Current trauma protocols utilize …

Three-dimensional liver image segmentation using generative adversarial networks based on feature restoration

R He, S Xu, Y Liu, Q Li, Y Liu, N Zhao, Y Yuan… - Frontiers in …, 2022 - frontiersin.org
Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-
aided diagnosis and treatment of liver cancer based on medical imaging, accurate …

[HTML][HTML] Composite acoustic hole segmentation by semi-supervised learning for robotic multi-spindle drilling of aero-engine nacelle acoustic liners

Q Dong, B Mei, Y Fu, Y Yang, W Zhu - Composites Part A: Applied Science …, 2024 - Elsevier
The large-scale tiny acoustic holes densely distributed on acoustic liners are essential in
aero-engine noise reduction. Accurate segmentation of those holes is fundamental for a …

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images

Z Gong, C Guo, W Guo, D Zhao, W Tan… - Journal of Applied …, 2022 - Wiley Online Library
Accurate liver segmentation is essential for radiation therapy planning of hepatocellular
carcinoma and absorbed dose calculation. However, liver segmentation is a challenging …

Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7 T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3 T MRI

X Li, L Wang, H Liu, B Ma, L Chu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Precise delineation of hippocampus subfields is crucial for the identification and
management of various neurological and psychiatric disorders. However, segmenting these …

[HTML][HTML] A Review of Advancements and Challenges in Liver Segmentation

D Wei, Y Jiang, X Zhou, D Wu, X Feng - Journal of Imaging, 2024 - mdpi.com
Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring,
and surgical planning due to the complex anatomical structure and physiological functions …