CBCT-guided Prostate Adaptive Radiotherapy with CBCT-based Synthetic MRI and CT

Yang, Xiaofeng; Lei, Yang; Wang, T.; Liu, Y.; Tian, Sibo; Dong, Xue; Jiang, Xiaojun; Jani, Ashesh B.; Patel, Pretesh; Liu, T.

doi:10.1016/j.ijrobp.2019.06.372

Cited by 6 publications

(7 citation statements)

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“…14,15 Many steps in ART are dependent on fast and accurate organs-at-risk (OAR) and target delineation on CBCT. [16][17][18] For example, rapid target and OARs contouring provides important information, when used for image registration, can guide couch translation and rotation to reduce patient setup errors. 19 Compared to whole-image rigid registration, organ mask-aided nonrigid image registration is more accurate for guiding couch movement by focusing on clinically relevant regions such as the target and OARs.…”

Section: Introductionmentioning

confidence: 99%

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Pelvic multi‐organ segmentation on cone‐beam CT for prostate adaptive radiotherapy

Lei

Wang

et al. 2020

Medical Physics

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Background and purpose The purpose of this study is to develop a deep learning‐based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone‐beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning. Materials and methods We propose to utilize both CBCT and CBCT‐based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle‐consistent adversarial networks (CycleGAN), which was trained using paired CBCT‐MR images. To combine the advantages of both CBCT and sMRI, we developed a cross‐modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT‐specific and sMRI‐specific features prior to combining them in a late‐fusion network for final segmentation. The network was trained and tested using 100 patients’ datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations. Results For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 ± 0.03, 0.65 ± 0.67 mm; 0.91 ± 0.08, 0.93 ± 0.96 mm; 0.93 ± 0.04, 0.72 ± 0.61 mm; 0.95 ± 0.05, 1.05 ± 1.40 mm; and 0.95 ± 0.05, 1.08 ± 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy. Conclusion We developed a deep learning‐based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs‐at‐risk contouring for prostate adaptive radiation therapy.

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Section: Introductionmentioning

confidence: 99%

“…Many steps in ART are dependent on fast and accurate organs‐at‐risk (OAR) and target delineation on CBCT 16–18 . For example, rapid target and OARs contouring provides important information, when used for image registration, can guide couch translation and rotation to reduce patient setup errors 19 .…”

Section: Introductionmentioning

confidence: 99%

Pelvic multi‐organ segmentation on cone‐beam CT for prostate adaptive radiotherapy

Lei

Wang

et al. 2020

Medical Physics

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“…Registration of the CBCT images with respect to the planning CT images indicates the correctness of the patient positioning and provides feedbacks for any needed adjustment. Recent advances in the deep learning-based CBCT/CT registration methods have been developed to aid fast patient set up and pretreatment positioning (63,(81)(82)(83). For example, Liao et al used reinforcement learning (RL) to perform rigid image registration between CT and CBCT images (63).…”

Section: Volumetric Imaging-based Localizationmentioning

confidence: 99%

“…Hence, CBCT/ CT registration usually relies on the high-contrast bony structure or implanted fiducials. To mitigate this limitation, synthetic CT images can be generated from daily CBCT images using deep learning-based approaches (81,84,85). The synthetic CT images usually have comparable accuracy of HU values compared to the planning CT image, as shown in Figure 3.…”

Section: Volumetric Imaging-based Localizationmentioning

confidence: 99%

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Zhao¹,

Shen²,

Islam³

et al. 2021

Quant Imaging Med Surg

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Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

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“…Inspired by the success of DL in computer vision, researchers have attempted to extend the DL-based techniques to medical imaging. DL-based methods have been extensively explored in medical imaging for the purposes of segmentation [19,21,72,65,66,70,71,74,77,172,140,141,142,146,147,150,153,159,148], synthesis [22,67,73,76,86,87,88,119,120,149,169,167,170], enhancement and correction [20,39,151,152,145,148,168,174,175], and registration [26,120,72,169,172]. DL-based multi-organ segmentation techniques represent a significant innovation in daily practices of radiation therapy, expediting the segmentation process, enhancing contour consistency and promoting compliance to delineation guidelines…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Multi-organ Segmentation

Yang¹,

Fu²,

Wang³

et al. 2020

Preprint

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This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and their applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multi-organ segmentation using benchmark datasets, including the 2017 AAPM Thoracic Autosegmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.

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CBCT-guided Prostate Adaptive Radiotherapy with CBCT-based Synthetic MRI and CT

Cited by 6 publications

References 0 publications

Pelvic multi‐organ segmentation on cone‐beam CT for prostate adaptive radiotherapy

Pelvic multi‐organ segmentation on cone‐beam CT for prostate adaptive radiotherapy

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Deep Learning in Multi-organ Segmentation

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