A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation

Nie, Shihong; Wei, Yuanfeng; Zhao, Fen; Dong, Ya; Chen, Yan; Li, Qiaoqi; Du, Wei; Li, Xin; Yang, Xi; Li, Zhiping

doi:10.1186/s13014-022-02157-5

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…Previously, studies have reported the results of autosegmentation of OAR/CTVs in cervical cancer (15,(23)(24)(25)(26). One study including 100 cases for model training reported similar results to those of our study for OARs (24).…”

Section: Discussionsupporting

confidence: 84%

“…A study by Li et al employed quantitative metrics, including the DSC, HD, and true positive volume fraction, to analyze the contours of the CTV, bladder, rectum, bowel bag, and femoral head in postoperative cases. The DSC values ranged from 0.84 to 0.93 (25). In another study, in addition to DSC and HD, the Jaccard coefficient and dose-volume index were utilized for dosimetric evaluation of the spinal cord, kidney, bladder, femoral head, pelvic bone, rectum, and small intestine (26).…”

Section: Discussionmentioning

confidence: 99%

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Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

2023

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Background and purposeDeep learning-based models have been actively investigated for various aspects of radiotherapy. However, for cervical cancer, only a few studies dealing with the auto-segmentation of organs-at-risk (OARs) and clinical target volumes (CTVs) exist. This study aimed to train a deep learning-based auto-segmentation model for OAR/CTVs for patients with cervical cancer undergoing radiotherapy and to evaluate the model’s feasibility and efficacy with not only geometric indices but also comprehensive clinical evaluation.Materials and methodsA total of 180 abdominopelvic computed tomography images were included (training set, 165; validation set, 15). Geometric indices such as the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD) were analyzed. A Turing test was performed and physicians from other institutions were asked to delineate contours with and without using auto-segmented contours to assess inter-physician heterogeneity and contouring time.ResultsThe correlation between the manual and auto-segmented contours was acceptable for the anorectum, bladder, spinal cord, cauda equina, right and left femoral heads, bowel bag, uterocervix, liver, and left and right kidneys (DSC greater than 0.80). The stomach and duodenum showed DSCs of 0.67 and 0.73, respectively. CTVs showed DSCs between 0.75 and 0.80. Turing test results were favorable for most OARs and CTVs. No auto-segmented contours had large, obvious errors. The median overall satisfaction score of the participating physicians was 7 out of 10. Auto-segmentation reduced heterogeneity and shortened contouring time by 30 min among radiation oncologists from different institutions. Most participants favored the auto-contouring system.ConclusionThe proposed deep learning-based auto-segmentation model may be an efficient tool for patients with cervical cancer undergoing radiotherapy. Although the current model may not completely replace humans, it can serve as a useful and efficient tool in real-world clinics.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

2023

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“…Recently, deep‐learning‐based (DL) methods using Convolutional neural networks (CNN) have been applied to the segmentation of images of a variety of cancers 6–8 . In terms of cervical cancer, it has shown comparable performance to the expert delineation for some cervical cancer segmentation 9–14 . However, the performance of these auto‐segmentation networks is highly dependent on the training dataset, which is affected by the difference in physician contouring styles and the anatomic structure differences among patients 15,16 .…”

Section: Introductionmentioning

confidence: 99%

“… 6 , 7 , 8 In terms of cervical cancer, it has shown comparable performance to the expert delineation for some cervical cancer segmentation. 9 , 10 , 11 , 12 , 13 , 14 However, the performance of these auto‐segmentation networks is highly dependent on the training dataset, which is affected by the difference in physician contouring styles and the anatomic structure differences among patients. 15 , 16 In order to generalize the trained network to a variety of unseen patient cases, network training is usually based on a large enough training set of different patient cases.…”

Section: Introductionmentioning

confidence: 99%

A prior‐information‐based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer

Wang,

Chang,

Pei

et al. 2024

J Applied Clin Med Phys

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ObjectiveAdaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient‐specific data was integrateda into a registration‐guided multi‐channel multi‐path (Rg‐MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation.MethodsThis study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg‐MCMP segmentation framework, the first‐course CT images (CT1) were registered to second‐course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U‐Net consisting of a channel‐based multi‐path feature extraction network. The performance of the Rg‐MCMP segmentation framework was evaluated and compared with the single‐channel single‐path model (SCSP), the standalone registration methods, and the registration‐guided multi‐channel single‐path (Rg‐MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics.ResultsThe average DSC of CTV for the deformable image DIR‐MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR‐MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR‐MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration‐guided multi‐channel single‐path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05).ConclusionThe proposed Rg‐MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

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“…Wu et al (2023) devised novel Inner Cascaded U-Net and Inner Cascaded U2-Net as improvements to plain cascaded U-Net for medical image segmentation, achieving better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as getting finer outline segmentation. Nie et al (2022) proposed a method named SegNet that was developed and trained with different data groups. Quantitative metrics and clinical-based grading were used to evaluate differences between several groups of automatic contours.…”

Section: Introductionmentioning

confidence: 99%

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

Zhang,

Sun,

Xie

et al. 2023

Front. Plant Sci.

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IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking.

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A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation

Cited by 8 publications

References 32 publications

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

A prior‐information‐based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

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