CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation

Ebadi, Nima; Li, Ruiqi; Das, Arun; Roy, Arkajyoti; Papanikolaou, Nikos; Rad, Paul

doi:10.1016/j.media.2023.102800

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…First, this study lacks a labeled dataset of all CBCT images for comparative experiments. One solution is to use a deformable registration algorithm, such as GT, to generate a large number of labels; however, this approach also introduces registration errors that are difficult to eliminate [39]. In this study, partial contouring CBCT data combined with labeled PCT were used for SSL segmentation research, and a small amount of labeled CTV data information and a large amount of unlabeled image information were learned to achieve the automatic delineation of CTV in CBCT images.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

Wang,

Cao,

Sun

et al. 2024

Radiat Oncol

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Objectives Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. Methods A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. Results The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. Conclusions Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

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Section: Discussionmentioning

confidence: 99%

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

Wang,

Cao,

Sun

et al. 2024

Radiat Oncol

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“…This framework incorporates epistemic uncertainty to identify regions of low confidence in the model's predictions. Moreover, Ebadi et al [28] crafted a deep learning architecture for the sequential analysis of cancer tumor progression using Cone Beam Computed Tomography (CBCT) images. This model incorporates an attention mechanism and provides uncertainty estimates for segmentation tasks, contributing to risk management in treatment planning and enhancing the model's calibration and reliability.…”

Section: Uncertainty-based Transfer Learning Technologymentioning

confidence: 99%

Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

Yu,

Pei

2024

Applied Sciences

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Deep learning has achieved remarkable progress in medical image analysis, but its effectiveness heavily relies on large-scale and well-annotated datasets. However, assembling a large-scale dataset of annotated histopathological images is challenging due to their unique characteristics, including various image sizes, multiple cancer types, and staining variations. Moreover, strict data privacy in medicine severely restricts data sharing and poses significant challenges in acquiring large-scale and well-annotated histopathological images. To tackle these constraints, Transfer Learning (TL) provides a promising solution by exploiting knowledge from another domain. This study proposes the Uncertainty-guided asymmetric Consistency Domain Adaptation (UCDA), which does not require accessing the source data and is composed of two essential components, e.g., Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL). In detail, USTL facilitates a secure mapping of the source domain model’s feature space onto the target domain, eliminating the dependency on source domain data to protect data privacy. At the same time, the ACL module measures the symmetry and asymmetry between the source and target domains, bridging the information gap and preserving inter-domain differences among medical images. We comprehensively evaluate the effectiveness of UCDA on three widely recognized and publicly available datasets, namely NCTCRC-HE-100K, PCam, and LC25000. Impressively, our proposed method achieves remarkable performance on accuracy and F1-scores. Additionally, feature visualizations effectively demonstrate the exceptional generalizability and discriminative power of the learned representations. These compelling results underscore the significant potential of UCDA in driving the advancement of deep learning techniques within the realm of histopathological image analysis.

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“…As HyperSight contains two modes for IGRT and CBCT‐based planning, it is expected that HyperSight will provide both rapid CBCT scans for more accurate patient motion management with IGRT mode and enhanced image quality for dose calculation of treatment planning with its CBCT for planning (CBCTp) mode. Since the performance of the OBI systems including CBCT is very important for IGRT and ART as previously mentioned, 8 , 9 the improvements in imaging performance have been of great interest.…”

Section: Introductionmentioning

confidence: 99%

Image quality characterization of an ultra‐high‐speed kilovoltage cone‐beam computed tomography imaging system on an O‐ring linear accelerator

Kim,

Park,

Zhao

et al. 2024

J Applied Clin Med Phys

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PurposeThe quality of on‐board imaging systems, including cone‐beam computed tomography (CBCT), plays a vital role in image‐guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O‐ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance.MethodsThe HyperSight features ultra‐fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning (CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software.ResultsHyperSight demonstrated substantial improvements in contrast‐to‐noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra‐fast scan time (5.9 s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience.ConclusionsHyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra‐fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

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CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation

Cited by 8 publications

References 42 publications

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

Uncertainty-Guided Asymmetric Consistency Domain Adaptation for Histopathological Image Classification

Image quality characterization of an ultra‐high‐speed kilovoltage cone‐beam computed tomography imaging system on an O‐ring linear accelerator

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