Segmentation of the cervical lesion region in colposcopic images based on deep learning

Yu, Hua; Fan, Yinuo; Ma, Huizhan; Zhang, Haifeng; Cao, Chengcheng; Yu, Xuyao; Sun, Jinglai; Cao, Yuzhen; Liu, Yuzhen

doi:10.3389/fonc.2022.952847

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…Cervical cancer is easily preventable with early screening and diagnosis. It is acknowledged that the bulk of deep learning-based artificial intelligence has been used in cytology, colposcopy, and DNA methylation research for cervical intraepithelial lesions [28][29][30]. Jian et al [31] showed that machine learning can identify methylation signals associated with the development of cervical cancer at qualitative and quantitative levels.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Ding

et al. 2023

Diagnostics

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Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889–0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921–0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Ding

et al. 2023

Diagnostics

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“…The Region of Interest’s (ROI) imaging signals can be filtered. In terms of image segmentation, the consensus typically acknowledges that image segmentation divides an image into smaller regions or objects based on its elements and this is dependent on the tissue of interest [ 39 , 40 ]. The precision of the image segmentation phase will heavily influence the efficacy and efficiency of the subsequent stages in image processing [ 41 ].…”

Section: Radiogenomics and Its Use In Precision Medicinementioning

confidence: 99%

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Demetriou,

Lockhat,

Brzozowski

et al. 2024

Cancers

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Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

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“…Promising solutions to this problem are offered by colposcopy-assisted examinations using artificial intelligence. A cervical lesion segmentation model (CLS-Model) was proposed by Yu et al [ 12 ] for the segmentation of the cervical lesion region from colposcopic post-acetic-acid images. According to this paper, precise segmentation results might offer a strong starting point for future studies on lesion identification and biopsy site choices.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

et al. 2022

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The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.

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Segmentation of the cervical lesion region in colposcopic images based on deep learning

Cited by 11 publications

References 29 publications

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

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