Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network

Oh, Kyeong Taek; Lee, Sang‐Won; Lee, Haeun; Yun, Mijin; Yoo, Sun Kook

doi:10.1007/s10278-020-00321-5

Cited by 24 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conditional-GAN 2014 21 Cardiac: [101][102][103][104][105][106][107] Brain: [108][109][110][111][112][113][114] Microscopic: [107,115,116] Orthopedic: [117] Skin: [118,119] Breast: [120] Retina: [121] Brain: [111] Cycle-GAN 2017 09 Microscopic: [122][123][124] Brain: [125][126][127] Cardiac: [128,129] Multi-Organ: [130] Pix2Pix-GAN 2016 07 Multi-Organ: [131] Microscopic: [132] Brain: [133,134] Retina: [135] Liver: [136] Bone: [137] Patch-GAN 2017 04 Retina: [135,138] Bone: [139] Brain: [140] Wasserstein-GAN 2017 02 Breast: [141] Brain: [142] Style-GAN 2019 01 Lung: [143] DC-GAN 2015 01 Skin: [144] metrics are intended to compare segm...…”

Section: Performance Metricsmentioning

confidence: 99%

“…Brain studies: , [108][109][110][111][112][113][114], [125][126][127], [133][134], [140], [142] Heart studies: [37], [88][89][90], [102], [105], [128], [129], [149] Fig. 2 Papers are classified by various biomedical imaging modalities MRI images.…”

Section: X-ray Mris Ultrasounds Ct Scansmentioning

confidence: 99%

“…The ADNI database's central object is to design a clinical system for prompt diagnosing of Alzheimers life-threatening disease in the early stage. Most of the medial datasets of ADNI are MRI and PET base images for disease assessments utilized in studies [25,110,126,134,142]. Likewise, studies in [24] and [26] used another dataset NAMIC Brain Multimodality for automatic brain segmentation.…”

Section: X-ray Mris Ultrasounds Ct Scansmentioning

confidence: 99%

See 2 more Smart Citations

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Iqbal

Sharif

Yasmin

et al. 2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

show abstract

Section: Performance Metricsmentioning

confidence: 99%

Section: X-ray Mris Ultrasounds Ct Scansmentioning

confidence: 99%

Section: X-ray Mris Ultrasounds Ct Scansmentioning

confidence: 99%

See 1 more Smart Citation

Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

Iqbal

Sharif

Yasmin

et al. 2022

Int J Multimed Info Retr

View full text Add to dashboard Cite

show abstract

“…Hu et al utilized a 3-D CNN network to conduct a semiautomated segmentation of the liver by learning subject-specific probability maps from CT images [ 86 ]. A generative adversarial network (GAN) network was trained to segment white matter from only brain PET images [ 87 ]. For tumor segmentation, a 3-D U-Net CNN network was trained to automatically detect and segment brain lesions on PET images with a 0.88 and 0.99 specificity at the voxel level [ 88 ].…”

Section: Overview Of Deep Learning Applications In Medical Imagingmentioning

confidence: 99%

Application and Construction of Deep Learning Networks in Medical Imaging

Torres-Velázquez

Chen

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this paper reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.

show abstract

“…In addition, the AI approach will take into account a wide range of paraclinical and clinical factors all of which are not routinely considered or easy to interpret. [82][83][84] In the long run, high-level AI will probably reach beyond the role of advanced medical decision aid by providing intelligent computer generated suggestions for alternative diagnosis and treatment regimens. However, underlying paraclinical and clinical data must be permanently accessible to AI-based algorithms, and such data need to be updated continuously -a matter that in times of increased data protection formalities remains to be solved judiciously.…”

Section: Artificial Intelligence-based Interpretationmentioning

confidence: 99%