Low-Rank Graph Regularized Sparse Coding

Zhang, Yupei; Liu, Shuhui; Shang, Xuequn; Xiang, Ming

doi:10.1007/978-3-319-97304-3_14

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The multi‐needle detection method, ORDL, proposed by Zhang et al employed a graph dictionary learning method39 to learn the feature set that excludes needle features from the no‐needle US images, and then rebuilt the needle images to obtain the difference between the original images and the reconstructed images, followed by refinements on the difference images corresponding to needles. Here, we trained ORDL on 71 no‐needle images and tested on our 23 needle images.…”

Section: Resultsmentioning

confidence: 99%

Multi‐needle Localization with Attention U‐Net in US‐guided HDR Prostate Brachytherapy

et al. 2020

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Purpose: Ultrasound (US)-guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. However, due to low signal-to-noise ratio (SNR) in US imaging, real-time multi-needle segmentation in 3D TRUS, which is the major obstacle for real-time dose mapping, has not been realized to date. In this study, we propose a deep learning-based method that enables accurate and real-time digitization of the multiple needles in the 3D TRUS images of HDR prostate brachytherapy. Methods: A deep learning model based on the U-Net architecture was developed to segment multiple needles in the 3D TRUS images. Attention gates were considered in our model to improve the prediction on the small needle points. Furthermore, the spatial continuity of needles was encoded into our model with total variation (TV) regularization. The combined network was trained on 3D TRUS patches with the deep supervision strategy, where the binary needle annotation images were provided as ground truth. The trained network was then used to localize and segment the HDR needles for a new patient's TRUS images. We evaluated our proposed method based on the needle shaft and tip errors against manually defined ground truth and compared our method with other state-ofart methods (U-Net and deeply supervised attention U-Net). Results: Our method detected 96% needles of 339 needles from 23 HDR prostate brachytherapy patients with 0.290 AE 0.236 mm at shaft error and 0.442 AE 0.831 mm at tip error. For shaft localization, our method resulted in 96% localizations with less than 0.8 mm error (needle diameter is 1.67 mm), while for tip localization, our method resulted in 75% needles with 0 mm error and 21% needles with 2 mm error (TRUS image slice thickness is 2 mm). No significant difference is observed (P = 0.83) on tip localization between our results with the ground truth. Compared with U-Net and deeply supervised attention U-Net, the proposed method delivers a significant improvement on both shaft error and tip error (P < 0.05). Conclusions: We proposed a new segmentation method to precisely localize the tips and shafts of multiple needles in 3D TRUS images of HDR prostate brachytherapy. The 3D rendering of the needles could help clinicians to evaluate the needle placements. It paves the way for the development of real-time plan dose assessment tools that can further elevate the quality and outcome of HDR prostate brachytherapy.

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Section: Resultsmentioning

confidence: 99%

Multi‐needle Localization with Attention U‐Net in US‐guided HDR Prostate Brachytherapy

et al. 2020

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“…As shown in FIGURE 1, LogSC produces similar sparse codes for the three face images. This work extends our previous conference paper [32] by providing theory explanations and analysis, more algorithm details and more experimental evaluations. This rest is organized as: Section 2 reviews basic ingredients.…”

Section: B Our Contributions and Paper Organizationmentioning

confidence: 53%

“…Rather than using the route of GSC, we combine the LRR learning with the GSC into a uniform optimization objective. The proposed method is dubbed Low-rank graph regularized Sparse Coding (LogSC) [32]. Our major contributions lie in:…”

Section: B Our Contributions and Paper Organizationmentioning

confidence: 99%

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

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Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies. While the traditional graph sparse coding preserves the neighborhood structure of the data, this study integrates the low-rank representation(LRR) to fix the inconsistency of sparse coding by holding the subspace structures of the high-dimensional observations. The proposed method is dubbed low-rank graph regularized sparse coding (LogSC), which learns sparse codes and low-rank representations jointly rather than the traditional two-step approach. Since the two data representations share a dictionary matrix, the resulted sparse representation on this dictionary could be benefited from LRR. We solved the optimization problem of LogSC by using the linearized alternating direction method with adaptive penalty. Experimental results show the proposed method is discriminative in feature learning and robust to various noises. This work provides a one-step approach to integrating graph embedding in representation learning. INDEX TERMS Graph sparse coding, graph embedding, data structure preserving, robust data representation, image representation, one-step integrated graph sparse coding.

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“…Since the L 2 -norm based reconstruction is sensitive to outliers and data corruptions, Lin et al proposes to use L 1 -norm instead of L 2 -norm to enhance the robustness [26][27][28]. Besides, we often have massively available side information data in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

Zhang

Dai

et al. 2020

Applied Sciences

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Student grade prediction (SGP) is an important educational problem for designing personalized strategies of teaching and learning. Many studies adopt the technique of matrix factorization (MF). However, their methods often focus on the grade records regardless of the side information, such as backgrounds and relationships. To this end, in this paper, we propose a new MF method, called graph regularized robust matrix factorization (GRMF), based on the recent robust MF version. GRMF integrates two side graphs built on the side data of students and courses into the objective of robust low-rank MF. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. The resulting objective problem can be effectively optimized by the Majorization Minimization (MM) algorithm. In addition, GRMF not only can yield the specific features for the education domain but can also deal with the case of missing, noisy, and corruptive data. To verify our method, we test GRMF on two public data sets for rating prediction and image recovery. Finally, we apply GRMF to educational data from our university, which is composed of 1325 students and 832 courses. The extensive experimental results manifestly show that GRMF is robust to various data problem and achieves more effective features in comparison with other methods. Moreover, GRMF also delivers higher prediction accuracy than other methods on our educational data set. This technique can facilitate personalized teaching and learning in higher education.

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Low-Rank Graph Regularized Sparse Coding

Cited by 4 publications

References 19 publications

Multi‐needle Localization with Attention U‐Net in US‐guided HDR Prostate Brachytherapy

Multi‐needle Localization with Attention U‐Net in US‐guided HDR Prostate Brachytherapy

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

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