Nuclei segmentation is a challenging task in histopathology images.It is challenging due to the small size of objects,low contrast,touching boundaries,and complex structure of nuclei.Their segmentation and counting pl...Nuclei segmentation is a challenging task in histopathology images.It is challenging due to the small size of objects,low contrast,touching boundaries,and complex structure of nuclei.Their segmentation and counting play an important role in cancer identification and its grading.In this study,WaveSeg-UNet,a lightweight model,is introduced to segment cancerous nuclei having touching boundaries.Residual blocks are used for feature extraction.Only one feature extractor block is used in each level of the encoder and decoder.Normally,images degrade quality and lose important information during down-sampling.To overcome this loss,discrete wavelet transform(DWT)alongside maxpooling is used in the down-sampling process.Inverse DWT is used to regenerate original images during up-sampling.In the bottleneck of the proposed model,atrous spatial channel pyramid pooling(ASCPP)is used to extract effective high-level features.The ASCPP is the modified pyramid pooling having atrous layers to increase the area of the receptive field.Spatial and channel-based attention are used to focus on the location and class of the identified objects.Finally,watershed transform is used as a post processing technique to identify and refine touching boundaries of nuclei.Nuclei are identified and counted to facilitate pathologists.The same domain of transfer learning is used to retrain the model for domain adaptability.Results of the proposed model are compared with state-of-the-art models,and it outperformed the existing studies.展开更多
Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled comput...Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled computational pathology.However,there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images,which can match physician-level basic perception.In this study,we design a deep learning framework,thyroid segmentation and hierarchy fine-needle aspiration(TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image.In the TshFNA-Examiner,cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network;cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)system,by a classification neural network which is further enhanced by leveraging unlabeled data.A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied,with pixel-wise and image-wise image patches annotated.Empirically,TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches.The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871.The cancer risk classifier achieves a macro-F1-score of 0.959,macro-AUC of 0.998,and accuracy of 0.959 following TBSRTC.The corresponding metrics can be enhanced to a macro-F1-score of 0.970,macro-AUC of 0.999,and accuracy of 0.970 by leveraging informative unlabeled data.In clinical practice,TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.展开更多
This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automat...This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.展开更多
近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线...近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。展开更多
Among the existing research on the treatment of disorders of consciousness(DOC),deep brain stimulation(DBS)offers a highly promising therapeutic approach.This comprehensive review documents the historical development ...Among the existing research on the treatment of disorders of consciousness(DOC),deep brain stimulation(DBS)offers a highly promising therapeutic approach.This comprehensive review documents the historical development of DBS and its role in the treatment of DOC,tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis.The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions,providing a framework for refining DBS targets.We also discuss the multimodal approaches for assessing patients with DOC,encompassing clinical behavioral scales,electrophysiological assessment,and neuroimaging techniques methods.During the evolution of DOC therapy,the segmentation of central nuclei,the recording of single-neurons,and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment.Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC,linking neuron-level dynamics with macroscopic behavioral changes.Despite showing promising outcomes,challenges remain in patient selection,precise target localization,and the determination of optimal stimulation parameters.Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC.It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks.Ultimately,by optimizing neuromodulation strategies,we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.展开更多
Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segm...Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages： classifier training,recognition and fine segmentation. In the classifier training,feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition,the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation,the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.展开更多
文摘Nuclei segmentation is a challenging task in histopathology images.It is challenging due to the small size of objects,low contrast,touching boundaries,and complex structure of nuclei.Their segmentation and counting play an important role in cancer identification and its grading.In this study,WaveSeg-UNet,a lightweight model,is introduced to segment cancerous nuclei having touching boundaries.Residual blocks are used for feature extraction.Only one feature extractor block is used in each level of the encoder and decoder.Normally,images degrade quality and lose important information during down-sampling.To overcome this loss,discrete wavelet transform(DWT)alongside maxpooling is used in the down-sampling process.Inverse DWT is used to regenerate original images during up-sampling.In the bottleneck of the proposed model,atrous spatial channel pyramid pooling(ASCPP)is used to extract effective high-level features.The ASCPP is the modified pyramid pooling having atrous layers to increase the area of the receptive field.Spatial and channel-based attention are used to focus on the location and class of the identified objects.Finally,watershed transform is used as a post processing technique to identify and refine touching boundaries of nuclei.Nuclei are identified and counted to facilitate pathologists.The same domain of transfer learning is used to retrain the model for domain adaptability.Results of the proposed model are compared with state-of-the-art models,and it outperformed the existing studies.
基金the National Natural Science Foundation of China(No.62102247)the Natural Science Foundation of Shanghai(No.23ZR1430700)。
文摘Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled computational pathology.However,there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images,which can match physician-level basic perception.In this study,we design a deep learning framework,thyroid segmentation and hierarchy fine-needle aspiration(TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image.In the TshFNA-Examiner,cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network;cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)system,by a classification neural network which is further enhanced by leveraging unlabeled data.A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied,with pixel-wise and image-wise image patches annotated.Empirically,TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches.The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871.The cancer risk classifier achieves a macro-F1-score of 0.959,macro-AUC of 0.998,and accuracy of 0.959 following TBSRTC.The corresponding metrics can be enhanced to a macro-F1-score of 0.970,macro-AUC of 0.999,and accuracy of 0.970 by leveraging informative unlabeled data.In clinical practice,TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.
文摘This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.
基金the National Natural Science Foundation of China(Nos.61171165,11431015 and 61571230)the Natural Science Foundation of Jiangsu Province(Nos.BK20161500 and BK20150784)+2 种基金the National Scientific Equipment Developing Project of China(No.2016YFF0103604)the China Postdoctoral Science Foundation(No.2015M581800)the Fundamental Research Funds for the Central Universities of China(No.30915012204)
文摘近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。
基金supported by the Science and Technology Innovation 2030(2022ZD0205300)the International(Hong Kong,Macao,and Taiwan)Science and Technology Cooperation Project(Z221100002722014)+5 种基金the 2022 Open Project of Key Laboratory and Engineering Technology Research of the Ministry of Civil Affairs(2022GKZS0003)the Chinese Institute for Brain Research Youth Scholar Program(2022-NKX-XM-02)the Natural Science Foundation of Beijing municipality(7232049)the General Program of National Natural Science Foundation of China(82371197)the FundRef Organization name of Guarantors of Brain(HMR04170)the Royal Society(IES\R3\213123).
文摘Among the existing research on the treatment of disorders of consciousness(DOC),deep brain stimulation(DBS)offers a highly promising therapeutic approach.This comprehensive review documents the historical development of DBS and its role in the treatment of DOC,tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis.The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions,providing a framework for refining DBS targets.We also discuss the multimodal approaches for assessing patients with DOC,encompassing clinical behavioral scales,electrophysiological assessment,and neuroimaging techniques methods.During the evolution of DOC therapy,the segmentation of central nuclei,the recording of single-neurons,and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment.Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC,linking neuron-level dynamics with macroscopic behavioral changes.Despite showing promising outcomes,challenges remain in patient selection,precise target localization,and the determination of optimal stimulation parameters.Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC.It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks.Ultimately,by optimizing neuromodulation strategies,we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.
基金supported by the National Natural Science Foundation of China(61673142,61471145,61305001)the Foundation of Education Department of Heilongjiang Province(12511096)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20132303120003)the Science Funds for the Young Innovative Talents of HUST(20152)
文摘Image segmentation directly determines the performance of automatic screening technique. However,there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem,a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages： classifier training,recognition and fine segmentation. In the classifier training,feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition,the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation,the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.
