Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions

Zaki, Nazar; Qin, Wenjian; Krishnan, Anusuya

doi:10.1016/j.aiopen.2023.08.006

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…A common cytological auxiliary screening method for cervical cancer is to promote the detection and grading of cervical cancer using graph-based methods based on the segmentation results of complex non-convex regions [49]. Bnouni et al [50] proposed a collection preconditioning method to realize the segmentation of cervical cancer cells based on a CNN.…”

Section: Segmentation Of Pathological Cellsmentioning

confidence: 99%

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Tang,

Zhang,

Gong

et al. 2023

Bioengineering

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Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.

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Section: Segmentation Of Pathological Cellsmentioning

confidence: 99%

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Tang,

Zhang,

Gong

et al. 2023

Bioengineering

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“…In the realm of applications in cervical cancer, several previous studies demonstrated that deep learning methods were developed to identify cervical cancer to improve diagnostic accuracy [10][11][12]. Specifically, models developed using MR data showed the potential of a universal model for cervical cancer identification and staging [13][14][15]. Applying a CNN to segment arteries in cervical PC MRI is challenging due to the number of arteries of interest (including right and left carotid and vertebral arteries) and the relatively low signal-to-noise ratio of the native images.…”

Section: Introductionmentioning

confidence: 99%

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Campbell,

Yadav,

Hussein

et al. 2023

Applied Sciences

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Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.

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Principal Graph Neighborhood Aggregation for Underwater Moving Object Detection

Kapoor,

Subudhi,

Jakhetiya

et al. 2024

Lecture Notes in Computer Science

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Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions

Cited by 7 publications

References 18 publications

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Principal Graph Neighborhood Aggregation for Underwater Moving Object Detection

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