Segmentation and Detection of Liver Tumors from CT Scans using TransUNet Architecture in Deep Learning DOI

Koushik Sundar,

Eashaan Manohar,

Vijay Kotra

et al.

Published: Aug. 28, 2024

Language: Английский

Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection DOI Creative Commons
Mihai Pomohaci, Mugur Grasu,

Alexandru-Ştefan Băicoianu-Nițescu

et al.

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 258 - 258

Published: Feb. 8, 2025

The liver is a frequent focus in radiology due to its diverse pathology, and artificial intelligence (AI) could improve diagnosis management. This systematic review aimed assess categorize research studies on AI applications from 2018 2024, classifying them according areas of interest (AOIs), task imaging modality used. We excluded reviews non-liver non-radiology studies. Using the PRISMA guidelines, we identified 6680 articles PubMed/Medline, Scopus Web Science databases; 1232 were found be eligible. A further analysis subgroup 329 focused detection and/or segmentation tasks was performed. Liver lesions main AOI CT most popular modality, while classification predominant task. Most (48.02%) used only public datasets, 27.65% one dataset. Code sharing practiced by 10.94% these articles. highlights predominance tasks, especially applied lesion imaging, often using imaging. Detection relied mostly external testing code lacking. Future should explore multi-task models dataset availability enhance AI’s clinical impact

Language: Английский

Citations

0
TULTS-Net: Local-Global Feature-Aware Transformer and inter-layer feature interaction for medical image processing DOI

MINGGE XIA,

Jinlin Ma, Ziping Ma

et al.

Digital Signal Processing, Journal Year: 2025, Volume and Issue: unknown, P. 105195 - 105195

Published: March 1, 2025

Language: Английский

Citations

0
Artificial intelligence techniques in liver cancer DOI Creative Commons
Lulu Wang, Mostafa Fatemi, Azra Alizad

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Sept. 3, 2024

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play crucial role in accurately evaluating HCC formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential supporting physicians by providing more accurate consistent diagnoses. Recent advancements led development of AI-based multi-modal prediction systems. These systems integrate with other modalities, such as electronic health record reports clinical parameters, enhance accuracy predicting biological characteristics prognosis, those associated HCC. pave way for response transarterial chemoembolization microvascular invasion treatments can assist clinicians identifying optimal patients who could benefit from interventional therapy. This paper provides an overview latest models developed diagnosing It also explores challenges future directions related application AI techniques.

Language: Английский

Citations

3
Segmentation and Detection of Liver Tumors from CT Scans using TransUNet Architecture in Deep Learning DOI

Koushik Sundar,

Eashaan Manohar,

Vijay Kotra

et al.

Published: Aug. 28, 2024

Language: Английский

Citations

0