Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Ding, Yi; Chen, Zhiran; Wang, Ziqi; Wang, Xiaohong; Hu, Desheng; Ma, Pingping; Ma, Chi; Wei, Wei; Li, Xiangbin; Xue, Xudong; Wang, Xiao

doi:10.1002/acm2.13566

Cited by 13 publications

(18 citation statements)

References 38 publications

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“…Collaborative research across multiple countries is crucial for advancing scientific knowledge and developing effective solutions to global challenges. However, our analysis of 77 original articles revealed that only a small proportion (18.18%) involved collaboration among authors from two [ 37 , 55 , 63 , 65 , 71 , 75 , 78 , 81 , 84 , 85 , 105 ] or three [ 70 , 91 , 92 ] different countries. This suggests that there is still a lack of international collaboration and data sharing in the field.…”

Section: Resultsmentioning

confidence: 89%

“…A limitation of the model-based approaches is potential biases in small datasets, such as studying gender-biased data [ 47 ]. Insufficient data to train the models was an issue in most of the studies in this review [ 13 , 39 , 44 , 45 , 47 , 65 , 90 , 105 , 106 , 107 ]. Lu et al, 2020 [ 66 ] adopted a strategy of transforming the image segmentation issue into a pixel-wise classification issue.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana

Stirling

Ros

et al. 2023

Cancers

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Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.

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Section: Resultsmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Paranavithana

Stirling

Ros

et al. 2023

Cancers

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“…sCT, on the other hand, had a significant difference between bladder contour ASD on CBCT and sCT. The ASD was less than two times the ASD of deep‐learning segmentations compared to manual delineations of bladder in female pelvis 26–28 . This variation can be due to the performance of anatomical preservation of the deep‐learning network 16,29 .…”

Section: Discussionmentioning

confidence: 94%

“…The ASD was less than two times the ASD of deep-learning segmentations compared to manual delineations of bladder in female pelvis. [26][27][28] This variation can be due to the performance of anatomical preservation of the deep-learning network. 16,29 If the anatomical preservation is low, it could influence plan selection.…”

Section: Discussionmentioning

confidence: 99%

Inter‐observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients

de Hond,

van Haaren,

Verrijssen

et al. 2023

J Applied Clin Med Phys

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IntroductionIn the Library‐of‐Plans (LoP) approach, correct plan selection is essential for delivering radiotherapy treatment accurately. However, poor image quality of the cone‐beam computed tomography (CBCT) may introduce inter‐observer variability and thereby hamper accurate plan selection. In this study, we investigated whether new techniques to improve the CBCT image quality and improve consistency in plan selection, affects the accuracy of LoP selection in cervical cancer patients.Materials and methodsCBCT images of 12 patients were used to investigate the inter‐observer variability of plan selection based on different CBCT image types. Six observers were asked to individually select a plan based on clinical X‐ray Volumetric Imaging (XVI) CBCT, iterative reconstructed CBCT (iCBCT) and synthetic CTs (sCT). Selections were performed before and after a consensus meeting with the entire group, in which guidelines were created. A scoring by all observers on the image quality and plan selection procedure was also included. For plan selection, Fleiss' kappa (κ) statistical test was used to determine the inter‐observer variability within one image type.ResultsThe agreement between observers was significantly higher on sCT compared to CBCT. The consensus meeting improved the duration and inter‐observer variability. In this manuscript, the guidelines attributed the overall results in the plan selection. Before the meeting, the gold standard was selected in 76% of the cases on XVI CBCT, 74% on iCBCT, and 76% on sCT. After the meeting, the gold standard was selected in 83% of the cases on XVI CBCT, 81% on iCBCT, and 90% on sCT.ConclusionThe use of sCTs can increase the agreement of plan selection among observers and the gold standard was indicated to be selected more often. It is important that clear guidelines for plan selection are implemented in order to benefit from the increased image quality, accurate selection, and decrease inter‐observer variability.

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“…Recently, the scope of auto-segmentation has been expanded to arterial intelligence (AI)-based contouring using deep learning algorithms (24)(25)(26)(27)(28)(29). Earlier work by Liu (30) demonstrated that the mean DSC values of deep learning-based methods were 0.924 for the bladder, 0.906 for the femoral head-L, 0.900 for the femoral head-R, 0.791 for the rectum, and 0.827 for the spinal cord.…”

Section: Discussionmentioning

confidence: 99%

The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy

Sun

et al. 2022

Front. Oncol.

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PurposeOur purpose was to investigate the influence of atlas library size and CT cross-slice number on the accuracy and efficiency of the atlas-based auto-segmentation (ABAS) method for the automatic contouring of clinical treatment volume (CTV) and organs at risk (OARs) during cervical cancer radiotherapy.MethodsOf 140 cervical cancer patients, contours from 20, 40, 60, 80, 100, and 120 patients were selected incrementally to create six atlas library groups in ABAS. Another 20 tested patients were automatically contoured with the ABAS method and manually contoured by the same professional oncologist. Contours included CTV, bladder, rectum, femoral head-L, femoral head-R, and spinal cord. The CT cross-slice numbers of the 20 tested patients included 61, 65, 72, 75, 81, and 84. The index of dice similarity coefficients (DSCs) and Hausdorff distance (HD) were used to assess the consistency between ABAS automatic contouring and manual contouring. The randomized block analysis of variance and paired t-test were used for statistical analysis.ResultsThe mean DSC values of “CTV, bladder, femoral head, and spinal cord” were all larger than 0.8. The femoral head and spinal cord showed a high degree of agreement between ABAS automatic contouring and manual contouring, with a mean DC >0.80 and HD <1 cm in all atlas library groups. A post-hoc least significant difference comparison indicated that no significant difference had been found between different atlas library sizes with DSC and HD values. For ABAS efficiency, the atlas library size had no effect on the time of ABAS automatic contouring. The time of automatic contouring increased slightly with the increase in CT cross-slice numbers, which were 99.9, 106.8, 114.0, 120.6, 127.9, and 134.8 s with CT cross-slices of 61, 65, 72, 75, 81, and 84, respectively.ConclusionA total of 20 atlas library sizes and a minimum CT cross-slice number including CTV and OARs are enough for ensuring the accuracy and efficiency of ABAS automatic contouring during cervical cancer radiotherapy.

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Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Cited by 13 publications

References 38 publications

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Inter‐observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients

The clinical evaluation of atlas-based auto-segmentation for automatic contouring during cervical cancer radiotherapy

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