Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation

Zhang, Jianwei; Liu, Zhen-Mei; Du, Bohai; He, Junting; Li, Guanzhao; Chen, Danni

doi:10.1016/j.compbiomed.2019.03.011

Cited by 28 publications

(6 citation statements)

References 26 publications

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“…Our study also indicated that some studies developed new networks based on existing widely used networks such as AlexNet, U-Net, ResNet, VGG, and GoogLeNet [13,42,43]. Since GAN was proposed, DCGAN [44], WGAN [45], CycleGAN [21], BEGAN [46], and BigBiGAN [47] were optimised for domain adaptation, data augmentation, and imageto-image translation.…”

Section: Discussionmentioning

confidence: 77%

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020

Wang

Huang

et al. 2022

European Journal of Radiology

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To evaluate the general rules and future trajectories of deep learning (DL) networks in medical image analysis through bibliometric and hot spot analysis of original articles published between 2012 and 2020. Methods: Original articles related to DL and medical imaging were retrieved from the PubMed database. For the analysis, data regarding radiological subspecialties; imaging techniques; DL networks; sample size; study purposes, setting, origins and design; statistical analysis; funding sources; authors; and first authors' affiliation was manually extracted from each article. The Bibliographic Item Co-Occurrence Matrix Builder and VOSviewer were used to identify the research topics of the included articles and illustrate the future trajectories of studies.Results: The study included 2685 original articles. The number of publications on DL and medical imaging has increased substantially since 2017, accounting for 97.2% of all included articles. We evaluated the rules of the application of 47 DL networks to eight radiological tasks on 11 human organ sites. Neuroradiology, thorax, and abdomen were frequent research subjects, while thyroid was under-represented. Segmentation and classification tasks were the primary purposes. U-Net, ResNet, and VGG were the most frequently used Convolutional neural network-derived networks. GAN-derived networks were widely developed and applied in 2020, and transfer learning was highlighted in the COVID-19 studies. Brain, prostate, and diabetic retinopathy-related studies were mature research topics in the field. Breast-and lung-related studies were in a stage of rapid development.Conclusions: This study evaluates the general rules and future trajectories of DL network application in medical image analyses and provides guidance for future studies.

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Section: Discussionmentioning

confidence: 77%

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020

Wang

Huang

et al. 2022

European Journal of Radiology

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“…Zhang et al [47] proposed a binary tree-like network with two-path fusion attention feature (BTTFA) for segmenting cervical cell nuclei. Due to the lack of real-world data for the cervical nucleus segmentation task, at the beginning of the work, they constructed a real-world clinical dataset including 104 LBC-based images with pixel-wise labels manually annotated by professional pathologists.…”

Section: Segmentation Of Nucleus and Cytoplasmmentioning

confidence: 99%

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future researches in automated cervical cytology screening.

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“…Células que não remetem a situac ¸ões reais, pois estas apresentam um maior nível de complexidade quanto a sobreposic ¸ão e artefatos na imagem. Temos ainda trabalhos envolvendo bases de dados com amostras coletadas em meio líquido [Zhang et al 2017, Zhang et al 2019, Allehaibi et al 2019, Wan et al 2019, Xiang et al 2020, Zou et al 2020. Embora sejam imagens reais, o meio líquido não é um método acessível para a maioria das mulheres no Brasil pois é de alto custo quando comparado ao convencional, assim países de média e baixa renda ainda enfrentam grandes desafios para sua implementac ¸ão.…”

Section: Base De Dados De Segmentac ¸ãOunclassified

Segmentação automática de núcleos cervicais em imagens de Papanicolaou

Teixeira

Rezende

Diniz

et al. 2022

Anais Do XXII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2022)

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A análise de células cervicais a partir de exames de Papanicolaou convencionais ainda é um grande desafio. Diferentemente das imagens de exame em meio líquido, a citologia convencional possui muita sobreposição celular e diversas estruturas epiteliais que dificultam a implementação de metodologias computacionais que possam dar suporte à automação dos exames. Este trabalho compara o desempenho de duas arquiteturas de redes neurais convolucionais para segmentar núcleos celulares. A avaliação considerou o uso de um novo banco de dados de segmentação. Os resultados mostraram que nossa proposta consegue segmentar núcleos celulares a partir de imagens de citologia convencional com múltiplas células e sobreposição.

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Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation

Cited by 28 publications

References 26 publications

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Segmentação automática de núcleos cervicais em imagens de Papanicolaou

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