Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations-Reference-Cited by-同舟云学术

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Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Published:2023-01 Issue: Volume:117 Page:105532
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Al-Kababji Ayman,Bensaali Faycal,Dakua Sarada Prasad,Himeur Yassine

Funder

Qatar National Research Fund

Qatar National Library

Qatar Foundation

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference198 articles.

1. TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach;Aghamohammadi;Expert Syst. Appl.,2021

2. Deep Belief Network Modeling for Automatic Liver Segmentation;Ahmad;IEEE Access,2019

3. A lightweight convolutional neural network model for liver segmentation in medical diagnosis;Ahmad;Comput. Intell. Neurosci.,2022

4. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer;Ahn;Radiat. Oncol.,2019

5. Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR;Al-Kababji,2022

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1. nnU-Net based segmentation and 3D reconstruction of uterine fibroids with MRI images for HIFU surgery planning;BMC Medical Imaging;2024-09-06

2. Liver vessel MRI image segmentation based on dual-path diffusion model;Journal of Radiation Research and Applied Sciences;2024-09

3. Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education;3D Printing in Medicine;2024-07-22

4. STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels;BMC Medical Imaging;2024-07-19

5. Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease;Applied Sciences;2024-02-12

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