2021
DOI: 10.1093/jrr/rrab070
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Automatic contour segmentation of cervical cancer using artificial intelligence

Abstract: In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the tumor contours would prove useful. To the best of our knowledge, automatic tumor contour segmentation has rarely been applied to cervical cancer treatment. In this study, diffusion-weighted images (DWI) of 98 patients with cervical cancer were acquired. We trained … Show more

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Cited by 20 publications
(10 citation statements)
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“…In the present study, the crude performance estimates are lower than that reported in some previous studies of CC tumor segmentation [10][11][12][13]. Bnouni et al reported a DSC of 0.93 (using T2-weighed MRI) [13] (n = 15), Kano et al reported a DSC score of 0.83 (using diffusion-weighted MRI) [11] (n = 98), and Lin et al reported a DSC score of 0.82 (using multiparametric MRI) [12] (n = 169). However, these studies all used k-fold cross-validation applied to a train/validation data set for performance estimation and hyperparameter tuning.…”
Section: Discussioncontrasting
confidence: 90%
See 1 more Smart Citation
“…In the present study, the crude performance estimates are lower than that reported in some previous studies of CC tumor segmentation [10][11][12][13]. Bnouni et al reported a DSC of 0.93 (using T2-weighed MRI) [13] (n = 15), Kano et al reported a DSC score of 0.83 (using diffusion-weighted MRI) [11] (n = 98), and Lin et al reported a DSC score of 0.82 (using multiparametric MRI) [12] (n = 169). However, these studies all used k-fold cross-validation applied to a train/validation data set for performance estimation and hyperparameter tuning.…”
Section: Discussioncontrasting
confidence: 90%
“…Thus, a seamless clinical integration of whole-volume radiomic tumor profiling requires the development of robust platforms for accurate automated tumor segmentation. Previous CC studies applying deep learning (DL) networks for automated primary tumor segmentation on MRI data report highly variable Dice scores (Dice scores: 0.44-0.93) between DL segmentation and tumor segmentation derived by radiologists [10][11][12][13]. Furthermore, poor reproducibility of certain radiomic parameters derived from automatic tumor segmentations have been reported [12].…”
Section: Introductionmentioning
confidence: 99%
“…However, the automatic segmentation of the gross tumor volume (GTV) represents a difficult task and has no clinical applications so far. To the best of our knowledge, there is only one recent study that relies on deep learning for automatic segmentation of the GTV in CC from DWI [37].…”
Section: Radiomic Workflowmentioning
confidence: 99%
“…Attempts to automatically segment cervical cancer have already been reported with moderate success. 7,8 If computer vision continues to evolve on par with the pace of other artificial intelligence subfields, such as natural language processing, which recently amazed the world with the introduction of ChatGPT, that might happen sooner rather than later.…”
mentioning
confidence: 99%
“…Once robust and validated neural networks obviate the manual segmentation step, the path will be cleared for integrating these radiomics markers and their interpretation (eg as the predicted risk by the model) directly into the scan report. Attempts to automatically segment cervical cancer have already been reported with moderate success 7,8 . If computer vision continues to evolve on par with the pace of other artificial intelligence subfields, such as natural language processing, which recently amazed the world with the introduction of ChatGPT, that might happen sooner rather than later.…”
mentioning
confidence: 99%