Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific stai...Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.展开更多
Deep learning based methods have demonstrated outstanding capabilities in quantifying nuclei and cells in microscopy images.However,differences among various stain modalities would affect the performance of nuclei det...Deep learning based methods have demonstrated outstanding capabilities in quantifying nuclei and cells in microscopy images.However,differences among various stain modalities would affect the performance of nuclei detection.How to fully utilize limited annotations for nuclei detection in other pathological staining images without annotations has become a significant challenge.This paper proposes an end-to-end unsupervised multi-level semantic consistent generative adversarial network(MSC-GAN)for nuclei detection across different pathological staining modalities.Specifically,we address nuclei detection on the unlabeled target domain data by first transforming the stain modality of the source domain into the target domain,and then utilizing the source domain annotations to train the nuclei detector network.A hierarchical semantic consistency loss including feature-level consistency and mask-level consistency is introduced to offer supplementary supervision to enhance the accuracy of generative adversarial learning.We further design an augmentation module to prevent the discriminator from overfitting.The experimental results on four microscopy image datasets demonstrate that MSC-GAN outperforms state-of-the-art methods in the nuclei detection tasks,achieving superior F1 scores.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.20720230037)the National Natural Science Foundation of China(No.52273305)+2 种基金Natural Science Foundation of Fujian Province of China(No.2023J05012)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(Nos.2023XAKJ0103071,2023XAKJ0102061)Natural Science Foundation of Xiamen,China(No.3502Z20227010).
文摘Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.
基金supported by the National Natural Science Foundation of China under Grant No.U21A20390the Development Project of Jilin Province of China under Grant No.20210509006RQthe Fundamental Research Funds for the Central Universities of China.
文摘Deep learning based methods have demonstrated outstanding capabilities in quantifying nuclei and cells in microscopy images.However,differences among various stain modalities would affect the performance of nuclei detection.How to fully utilize limited annotations for nuclei detection in other pathological staining images without annotations has become a significant challenge.This paper proposes an end-to-end unsupervised multi-level semantic consistent generative adversarial network(MSC-GAN)for nuclei detection across different pathological staining modalities.Specifically,we address nuclei detection on the unlabeled target domain data by first transforming the stain modality of the source domain into the target domain,and then utilizing the source domain annotations to train the nuclei detector network.A hierarchical semantic consistency loss including feature-level consistency and mask-level consistency is introduced to offer supplementary supervision to enhance the accuracy of generative adversarial learning.We further design an augmentation module to prevent the discriminator from overfitting.The experimental results on four microscopy image datasets demonstrate that MSC-GAN outperforms state-of-the-art methods in the nuclei detection tasks,achieving superior F1 scores.
