The overview of the deep learning integrated into the medical imaging of liver: a review

被引:8
作者
Xiang, Kailai [1 ,2 ]
Jiang, Baihui [3 ]
Shang, Dong [1 ,2 ]
机构
[1] Dalian Med Univ, Dept Gen Surg, Affiliated Hosp 1, Dalian 116011, Liaoning, Peoples R China
[2] Dalian Med Univ, Clin Lab Integrat Med, Affiliated Hosp 1, Dalian 116011, Liaoning, Peoples R China
[3] Dalian Med Univ, Dept Ophthalmol, Affiliated Hosp 1, Dalian 116011, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning; Artificial intelligence; Convolutional neural network; Liver disease; Ultrasonography; Computed tomography; Magnetic resonance imaging; Imaging diagnosis; Image segmentation; Lesion classification; CONVOLUTIONAL NEURAL-NETWORKS; ULTRASOUND; DIAGNOSIS; ELASTOGRAPHY; TUMOR; CLASSIFICATION; SEGMENTATION;
D O I
10.1007/s12072-021-10229-z
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.
引用
收藏
页码:868 / 880
页数:13
相关论文
共 83 条
[1]   Diagnosis and staging of hepatocellular carcinoma (HCC): current guidelines [J].
Ayuso, Carmen ;
Rimola, Jordi ;
Vilana, Ramon ;
Burrel, Marta ;
Darnell, Anna ;
Garcia-Criado, Angeles ;
Bianchi, Luis ;
Belmonte, Ernest ;
Caparroz, Carla ;
Barrufet, Marta ;
Bruix, Jordi ;
Bru, Concepcion .
EUROPEAN JOURNAL OF RADIOLOGY, 2018, 101 :72-81
[2]   Recurrent neural networks as versatile tools of neuroscience research [J].
Barak, Omri .
CURRENT OPINION IN NEUROBIOLOGY, 2017, 46 :1-6
[3]  
Ben-Cohen A., 2020, Handbook of medical image computing and computer assisted intervention, P65, DOI DOI 10.1016/B978-0-12-816176-0.00008-9
[4]  
BENCOHEN A, 2016, DEEP LEARN DATA LABE
[5]   Endoscopic ultrasound description of liver segmentation and anatomy [J].
Bhatia, Vikram ;
Hijioka, Susumu ;
Hara, Kazuo ;
Mizuno, Nobumasa ;
Imaoka, Hiroshi ;
Yamao, Kenji .
DIGESTIVE ENDOSCOPY, 2014, 26 (03) :482-490
[6]   Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm [J].
Biswas, Mainak ;
Kuppili, Venkatanareshbabu ;
Edla, Damodar Reddy ;
Suri, Harman S. ;
Saba, Luca ;
Marinhoe, Rui Tato ;
Sanches, J. Miguel ;
Suri, Jasjit S. .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 155 :165-177
[7]   Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images [J].
Byra, Michal ;
Styczynski, Grzegorz ;
Szmigielski, Cezary ;
Kalinowski, Piotr ;
Michalowski, Lukasz ;
Paluszkiewicz, Rafal ;
Ziarkiewicz-Wroblewska, Bogna ;
Zieniewicz, Krzysztof ;
Sobieraj, Piotr ;
Nowicki, Andrzej .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2018, 13 (12) :1895-1903
[8]   Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease [J].
Cao, Wen ;
An, Xing ;
Cong, Longfei ;
Lyu, Chaoyang ;
Zhou, Qian ;
Guo, Ruijun .
JOURNAL OF ULTRASOUND IN MEDICINE, 2020, 39 (01) :51-59
[9]   CAD and AI for breast cancer-recent development and challenges [J].
Chan, Heang-Ping ;
Samala, Ravi K. ;
Hadjiiski, Lubomir M. .
BRITISH JOURNAL OF RADIOLOGY, 2020, 93 (1108)
[10]   Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning [J].
Cho, Bum-Joo ;
Bang, Chang Seok ;
Lee, Jae Jun ;
Seo, Chang Won ;
Kim, Ju Han .
JOURNAL OF CLINICAL MEDICINE, 2020, 9 (06) :1-14