Advances in liver, liver lesion, hepatic vasculature, and biliary segmentation: a comprehensive review of traditional and deep learning approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F25%3AE0111787" target="_blank" >RIV/00843989:_____/25:E0111787 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/25:10258291 RIV/61989100:27740/25:10258291

Výsledek na webu

<a href="https://doi.org/10.1007/s10462-025-11310-x" target="_blank" >https://doi.org/10.1007/s10462-025-11310-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10462-025-11310-x" target="_blank" >10.1007/s10462-025-11310-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advances in liver, liver lesion, hepatic vasculature, and biliary segmentation: a comprehensive review of traditional and deep learning approaches

Popis výsledku v původním jazyce

Background and motivation: Liver segmentation plays a critical role in medical imaging, aiding in diagnosis, treatment planning, and surgical interventions for liver diseases. Precise segmentation of liver structures, including vessels, tumors, and other substructures, is essential for effective patient management. Traditional manual methods are time-consuming and prone to variability, prompting the development of automated techniques. This review aims to evaluate the evolution of liver segmentation methodologies, focusing on recent advancements in deep learning and hybrid approaches. Materials and methods: This review follows the PRISMA guidelines for systematic analysis, including a detailed database search across PubMed, Web of Science, Scopus, and IEEE Xplore. The search focused on segmentation techniques for various liver structures using deep learning, traditional methods, and hybrid models. A total of 7819 studies were initially identified, with 190 selected for detailed analysis based on inclusion criteria like Dice Similarity Coefficient (DSC) metrics and clinical applicability. Results: The analysis identified deep learning models, such as U-Net variants and Swin Transformer-based architectures, as leading methods for liver parenchyma and tumor segmentation, achieving DSC values up to 98.9% on benchmark datasets. For vessel segmentation, methods like DeepLabV3+ and the feature-based approaches demonstrated robustness across different datasets. Despite progress, challenges remain in segmenting structures like biliary ducts and hematomas due to limited annotated data and imaging variability. Discussion: While deep learning has significantly improved segmentation accuracy, challenges such as class imbalance and variability across imaging modalities persist. Hybrid approaches that combine traditional image processing with advanced neural networks show potential for further improvement. Future research should focus on enhancing generalizability through mult...

Název v anglickém jazyce

Advances in liver, liver lesion, hepatic vasculature, and biliary segmentation: a comprehensive review of traditional and deep learning approaches

Popis výsledku anglicky

Background and motivation: Liver segmentation plays a critical role in medical imaging, aiding in diagnosis, treatment planning, and surgical interventions for liver diseases. Precise segmentation of liver structures, including vessels, tumors, and other substructures, is essential for effective patient management. Traditional manual methods are time-consuming and prone to variability, prompting the development of automated techniques. This review aims to evaluate the evolution of liver segmentation methodologies, focusing on recent advancements in deep learning and hybrid approaches. Materials and methods: This review follows the PRISMA guidelines for systematic analysis, including a detailed database search across PubMed, Web of Science, Scopus, and IEEE Xplore. The search focused on segmentation techniques for various liver structures using deep learning, traditional methods, and hybrid models. A total of 7819 studies were initially identified, with 190 selected for detailed analysis based on inclusion criteria like Dice Similarity Coefficient (DSC) metrics and clinical applicability. Results: The analysis identified deep learning models, such as U-Net variants and Swin Transformer-based architectures, as leading methods for liver parenchyma and tumor segmentation, achieving DSC values up to 98.9% on benchmark datasets. For vessel segmentation, methods like DeepLabV3+ and the feature-based approaches demonstrated robustness across different datasets. Despite progress, challenges remain in segmenting structures like biliary ducts and hematomas due to limited annotated data and imaging variability. Discussion: While deep learning has significantly improved segmentation accuracy, challenges such as class imbalance and variability across imaging modalities persist. Hybrid approaches that combine traditional image processing with advanced neural networks show potential for further improvement. Future research should focus on enhancing generalizability through mult...

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Artificial intelligence review

ISSN

0269-2821

e-ISSN

1573-7462

Svazek periodika

58

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

95

Strana od-do

1-95

Kód UT WoS článku

001523768300001

EID výsledku v databázi Scopus

2-s2.0-105010165045