Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

Xie, Xin; Song, Yeguang; Yang, Feng; Yan, Hui; Wang, Shulian; Zhao, Xiuhua; Dai, Jianrong

doi:10.21037/qims-20-1141

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Manual segmentation of all images is generally used in radiomics studies, especially in MRI scans ( 44 ). The segmentation of prior knowledge regions we applied to knee OA DL models was influenced by the work of Hosseinzadeh et al and Xie et al ( 19 , 45 ), who concluded that prior zonal knowledge significantly affected the performance of DL models. Finally, we proved that the DL model established in this study performed better than did experienced radiologists, which indicates that the model could be a tool to help clinicians make accurate diagnoses and treatment decisions.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge

Li¹,

Xiao²,

Liu³

et al. 2023

Quant Imaging Med Surg

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Background: Knee osteoarthritis (OA) is harmful to people's health. Effective treatment depends on accurate diagnosis and grading. This study aimed to assess the performance of a deep learning (DL) algorithm based on plain radiographs in detecting knee OA and to investigate the effect of multiview images and prior knowledge on diagnostic performance. Methods: In total, 4,200 paired knee joint X-ray images from 1,846 patients (July 2017 to July 2020) were retrospectively analyzed. Kellgren-Lawrence (K-L) grading was used as the gold standard for knee OA evaluation by expert radiologists. The DL method was used to analyze the performance of anteroposterior and lateral plain radiographs combined with prior zonal segmentation to diagnose knee OA. Four groups of DL models were established according to whether they adopted multiview images and automatic zonal segmentation as the DL prior knowledge. Receiver operating curve analysis was used to assess the diagnostic performance of 4 different DL models.Results: The DL model with multiview images and prior knowledge obtained the best classification performance among the 4 DL models in the testing cohort, with a microaverage area under the receiver operating curve (AUC) and macroaverage AUC of 0.96 and 0.95, respectively. The overall accuracy of the DL model with multiview images and prior knowledge was 0.96 compared to 0.86 for an experienced radiologist. The combined use of anteroposterior and lateral images and prior zonal segmentation affected diagnostic performance. Conclusions:The DL model accurately detected and classified the K-L grading of knee OA. Additionally, multiview X-ray images and prior knowledge improved classification efficacy.

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Section: Discussionmentioning

confidence: 99%

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge

Li¹,

Xiao²,

Liu³

et al. 2023

Quant Imaging Med Surg

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“…This procedure can potentially be improved by other novel methods or deep learning methods in terms of precision and efficiency. [37][38][39] Fourth, the slice resolution of CT image in this study was limited to 5 mm due to clinical protocol. A finer thickness (<3 mm) will result in better registration accuracy, where the improvement is marginal with 1 mm according to previous reports.…”

Section: Discussionmentioning

confidence: 99%

“…Third, the traditional automatic methods for point detection (Harris Corner Detection) and contour segmentation (gray‐level thresholding segmentation) were less accurate, especially in low contrast regions. This procedure can potentially be improved by other novel methods or deep learning methods in terms of precision and efficiency 37–39 . Fourth, the slice resolution of CT image in this study was limited to 5 mm due to clinical protocol.…”

Section: Discussionmentioning

confidence: 99%

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

Xie

Song

et al. 2022

J Applied Clin Med Phys

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Background and purpose For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non‐correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial‐, region‐, and intensity‐based) were jointly used in DIR algorithm for improved accuracy. Materials and methods Three types of metrics were combined to form a single‐objective function in DIR algorithm. Fiducial‐based metric was used to minimize the distance between the corresponding point sets of two images. Region‐based metric was used to improve the overlap between the corresponding areas of two images. Intensity‐based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast‐conserving surgery and postoperative radiotherapy were enrolled in this study. Results For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary. Conclusions Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.

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“…All CTs were pre-processed using 3D Slicer (RRID:SCR_005619) [ 13 , 14 ]. They were first resampled to an isotropic resolution of 1 × 1 × 5 mm and then cropped to dimensions of 256 × 256 × 32 around the breast's centroid [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy

Xie,

Song,

et al. 2023

Radiat Oncol

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Background Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model. Methods To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance. Results The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05). Conclusions The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.

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Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

Cited by 7 publications

References 21 publications

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy

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