Accurate and rapid <scp>CT</scp> image segmentation of the eyes and surrounding organs for precise radiotherapy

Sun, Yeneng; Shi, Huabei; Zhang, Shuo; Wang, Pei; Zhao, Weiling; Zhou, Xiaobo; Yuan, Kehong

doi:10.1002/mp.13463

Cited by 18 publications

(14 citation statements)

References 20 publications

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“…After reviewing their abstracts, 49 were considered to be relevant and were further supplemented with selected publications from their list of references. In total, we collected 75 publications 22–96 focused on RT planning and three studies focused on hyperthermia therapy planning 97–99 from 2008 to date (Fig. 1), along with three review papers related to auto‐segmentation in the H&N region 19–21 .…”

Section: Resultsmentioning

confidence: 99%

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Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image databaseseveral image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performancethe best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

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Section: Resultsmentioning

confidence: 99%

“…Conventional CT [22][23][24][26][27][28][30][31][32][33][34][35][36][37][38][39][40][41][42][44][45][46][47][48][49][50][51][52][53][54][55][56][57][59][60][61][62][64][65][66][67][68][69][70]72,73,[76][77][78][79][80][81][82]…”

Section: Computed Tomography (Ct)unclassified

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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“…Zhu et al proposed an end-to-end atlas-free deep learning model and demonstrated an average DSC of 78.8% ( 19 ). Sun et al developed a locating-and-segmentation approach and achieved DSC values of 82.2–94% ( 20 ). For GTV segmentation, Lin et al developed a 3D CNN method to auto-contour GTV in MR images, and they demonstrated a DSC value of 0.79 ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Sequential and Iterative Auto-Segmentation of High-Risk Clinical Target Volume for Radiotherapy of Nasopharyngeal Carcinoma in Planning CT Images

et al. 2020

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Background: Accurate segmentation of tumor targets is critical for maximizing tumor control and minimizing normal tissue toxicity. We proposed a sequential and iterative U-Net (SI-Net) deep learning method to auto-segment the high-risk primary tumor clinical target volume (CTVp1) for treatment planning of nasopharyngeal carcinoma (NPC) radiotherapy. Methods: The SI-Net is a variant of the U-Net architecture. The input of SI-Net includes one CT image, the CTVp1 contour on this image, and the next CT image. The output is the predicted CTVp1 contour on the next CT image. We designed the SI-Net, using the left side to learn the volumetric features and the right to localize the contour on the next image. Two prediction directions, one from inferior to superior (forward direction) and the other from superior to inferior (backward direction), were tested. The performance was compared between the SI-Net and the U-Net using Dice similarity coefficient (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD) metrics. Results: The DSC and JI values from the forward direction SI-Net model were 5 and 6% higher than those from the U-Net model (0.84 ± 0.04 vs. 0.80 ± 0.05 and 0.74 ± 0.05 vs. 0.69 ± 0.05, p < 0.001). The smaller ASD and HD values also indicated a better performance (2.8 ± 1.0 vs. 3.3 ± 1.0 mm and 8.7 ± 2.5 vs. 9.7 ± 2.7 mm, p < 0.01) for the SI-Net model. For the backward direction SI-Net model, the DSC and JI values were still better than those from the U-Net model ( p < 0.01), although there were no significant differences in ASD and HD. Conclusions: The SI-Net model preserved the continuity between adjacent images and thus improved the segmentation accuracy compared with the conventional U-Net model. This model has potential of improving the efficiency and consistence of CTVp1 contouring for NPC patients.

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“…In recent years, deep learning‐based methods have been widely used in medical image segmentation. To make this technology serve more people, many commercial deep learning‐based autosegmentation software programs have emerged, such as Limbus Contour, 27 AiContour of Linking Med, 28 and DeepViewer. 29 When applied in clinical practice, the accuracy of autosegmentation has decreased to varying degrees due to data heterogeneity and IOV.…”

Section: Discussionmentioning

confidence: 99%

Clinical application and improvement of a CNN‐based autosegmentation model for clinical target volumes in cervical cancer radiotherapy

Chang

Zhi

Zhao

et al. 2021

J Applied Clin Med Phys

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Objective: Clinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN-based autosegmentation of CTV contours in cervical cancer. Methods: This study included 400 cervical cancer treatment planning cases with CTV delineated by radiation oncologists from three hospitals. The datasets were divided into five subdatasets (80 cases each). The cases in datasets 1, 2, and 3 were delineated by physicians A, B, and C, respectively. The cases in datasets 4 and 5 were delineated by multiple physicians. Dataset 1 was divided into training (50 cases), validation (10 cases), and testing (20 cases) cohorts, and they were used to construct the pretrained model. Datasets 2-5 were regarded as host datasets to evaluate the accuracy of the pretrained model. In the adaptive process, the pretrained model was fine-tuned to measure improvements by gradually adding more training cases selected from the host datasets. The accuracy of the autosegmentation model on each host dataset was evaluated using the corresponding test cases. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD_95) were used to evaluate the accuracy. Results: Before and after adaptive improvements, the average DSC values on the host datasets were 0.818 versus 0.882, 0.763 versus 0.810, 0.727 versus 0.772, and 0.679 versus 0.789, which are improvements of 7.82%, 6.16%, 6.19%, and 16.05%, respectively. The average HD_95 values were 11.143 mm versus 6.853 mm,22.402 mm versus 14.076 mm,28.145 mm versus 16.437 mm, and 33.034 mm versus 16.441 mm,which are improvements of 37.94%,37.17%, 41.60%, and 50.23%, respectively. Conclusion:The proposed method improved the adaptability of the CNNbased autosegmentation model when applied to host datasets.

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Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy

Cited by 18 publications

References 20 publications

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Sequential and Iterative Auto-Segmentation of High-Risk Clinical Target Volume for Radiotherapy of Nasopharyngeal Carcinoma in Planning CT Images

Clinical application and improvement of a CNN‐based autosegmentation model for clinical target volumes in cervical cancer radiotherapy

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