Unpaired virtual histological staining using prior-guided generative adversarial networks

Yan, Renao; He, Qiming; Liu, Yiqing; Ye, Peng; Zhu, Lianghui; Shu, Shi; Gou, Jizhou; He, Yonghong; Guan, Tian; Zhou, Guangde

doi:10.1016/j.compmedimag.2023.102185

Cited by 8 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we plan to continue exploring the system proposed in this article for diagnosing NAFLD in crab‐eating macaques. Future research should focus on addressing identified limitations and expanding the system's capabilities for clinical application [46]. Moreover, efforts to enhance the system's scalability and generalizability across diverse patient populations and histopathology datasets would enhance its impact on NAFLD diagnosis and management.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD

Fu,

Zang,

Lin

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

Nonalcoholic fatty liver disease (NAFLD) is a prevalent liver disorder affecting approximately 25.2% of the global population, posing risks of liver fibrosis, cancer, and metabolic disturbances. Despite its increasing prevalence, many facets of NAFLD's pathogenesis remain elusive, and there are currently no approved therapeutic drugs, underscoring the critical need for a comprehensive understanding of its pathophysiology to enable early diagnosis and intervention. Experimental animal studies play a pivotal role in elucidating the mechanisms underlying NAFLD and in the exploration of novel pharmacotherapies. Despite the widespread integration of deep learning techniques in human histopathology, their application to scrutinize histological features in animal models warrants exploration. This study presents a pioneering NAFLD assessment system leveraging IFNet and ResNet34 architectures. This automated system adeptly identifies inflammatory cell foci and hepatic steatosis in histopathology sections of rat livers. Remarkably, our approach achieved an impressive 95.6% accuracy in the assessment of hepatic steatosis and 77.7% in the evaluation of inflammation cell foci. By introducing a novel histopathology scoring system, our methodology mitigated subjective variations inherent in traditional pathologist evaluations, concurrently streamlining time and labor costs. This system ensured a standardized and precise assessment of rat liver histology in NAFLD and represented a significant stride toward enhancing the efficiency and objectivity of experimental outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD

Fu,

Zang,

Lin

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…[9][10][11] Recently, it has achieved comparable performance in pathological image analysis, including classification, 12 segmentation, 13 detection, 14 and assisted diagnosis. [15][16][17] Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc. At present, most studies of deep learning in renal pathology focus on glomeruli, which are limited to isolated analysis of single level or staining, including segmentation and detection [23][24][25] and classification.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

show abstract

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images

Yang,

He,

Wang

et al. 2024

Int J CARS

View full text Add to dashboard Cite

Unpaired virtual histological staining using prior-guided generative adversarial networks

Cited by 8 publications

References 31 publications

A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD

A Deep Learning Method Enables Quantitative and Automatic Measurement of Rat Liver Histology in NAFLD

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images

Contact Info

Product

Resources

About