Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning

Zhang, Yupei; He, Xiuxiu; Tian, Zhen; Jeong, Jiwoong; Lei, Yang; Wang, Tonghe; Zeng, Qiulan; Jani, Ashesh B.; Curran, Walter J.; Patel, Pretesh; Liu, Tian; Yang, Xiaofeng

doi:10.1109/tmi.2020.2968770

Cited by 35 publications

(27 citation statements)

References 38 publications

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“…In future work, we develop the proposed method in more applications. An order-graph regularized dictionary learning was proposed for 3D ultrasound image reconstruction, where LogSC could be used to further keep the low-rank structure of the needles in 2D slices [55]. Since similar students have similar grades and similar course have similar grades, the student grade matrix has a low-rank structure ignored by the graph robust matrix factorization [56].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Zhang

Liu

2020

IEEE Access

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“…This approach requires automatic multi-needle localization on TRUS images, which appears feasible given the encouraging results reported in recent studies. [3][4][5] Real-time dose evaluation and verification are then possible using the combination of automatically segmented structures and the localization needles/ seeds. These automation processes can tremendously reduce labor and time, reduce the dependency on operator experience, improve the consistency of plan quality and allow timely implant adjustments-auto-contouring on TRUS images is a vital part of these processes.…”

Section: Introductionmentioning

confidence: 99%

Male pelvic multi‐organ segmentation on transrectal ultrasound using anchor‐free mask CNN

et al. 2021

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Purpose Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs‐at‐risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning‐based method for male pelvic multi‐organ segmentation on transrectal ultrasound images. Methods We developed an anchor‐free mask convolutional neural network (CNN) that consists of three subnetworks, that is, a backbone, a fully convolutional one‐state object detector (FCOS), and a mask head. The backbone extracts multi‐level and multi‐scale features from an ultrasound (US) image. The FOCS utilizes these features to detect and label (classify) the volume‐of‐interests (VOIs) of organs. In contrast to the design of a previously investigated mask regional CNN (Mask R‐CNN), the FCOS is anchor‐free, which can capture the spatial correlation of multiple organs. The mask head performs segmentation on each detected VOI, where a spatial attention strategy is integrated into the mask head to focus on informative feature elements and suppress noise. For evaluation, we retrospectively investigated 83 prostate cancer patients by fivefold cross‐validation and a hold‐out test. The prostate, bladder, rectum, and urethra were segmented and compared with manual contours using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), mean surface distance (MSD), center of mass distance (CMD), and volume difference (VD). Results The proposed method visually outperforms two competing methods, showing better agreement with manual contours and fewer misidentified speckles. In the cross‐validation study, the respective DSC and HD95 results were as follows for each organ: bladder 0.75 ± 0.12, 2.58 ± 0.7 mm; prostate 0.93 ± 0.03, 2.28 ± 0.64 mm; rectum 0.90 ± 0.07, 1.65 ± 0.52 mm; and urethra 0.86 ± 0.07, 1.85 ± 1.71 mm. For the hold‐out tests, the DSC and HD95 results were as follows: bladder 0.76 ± 0.13, 2.93 ± 1.29 mm; prostate 0.94 ± 0.03, 2.27 ± 0.79 mm; rectum 0.92 ± 0.03, 1.90 ± 0.28 mm; and urethra 0.85 ± 0.06, 1.81 ± 0.72 mm. Segmentation was performed in under 5 seconds. Conclusion The proposed method demonstrated fast and accurate multi‐organ segmentation performance. It can expedite the contouring step of prostate brachytherapy and potentially enable auto‐planning and auto‐evaluation.

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“…However, few studies investigated the effects of education on brain development from the perspective of structural neuroimages. The medical image is a technique of probing the intrinsic structure of the human body that is often utilized in disease diagnosis and therapy (Zhang et al, 2020b(Zhang et al, , 2021b. While the GABA in the MFG was investigated (Zacharopoulos et al, 2021), we in this paper looked into the math-learning impact on brain development from the intraparietal sulcus (IPS) region that is also frequently reported in neuroimaging studies of arithmetic.…”

Section: Introductionmentioning

confidence: 99%

An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

2021

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This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the left middle front gyrus region of the brain have been investigated, we proposed to classify students by using MRIs from the intraparietal sulcus (IPS) region that was left untouched in the previous study. On the cropped IPS regions, the proposed model developed popular contrastive learning (CL) to solve the problem of multi-instance representation learning. The resulted data representations were then fed into a linear neural network to identify whether students were in the math group or the non-math group. Experiments were conducted on 123 adolescent students, including 72 math students and 51 non-math students. The proposed model achieved an accuracy of 90.24 % for student classification, gaining more than 5% improvements compared to the classical CL frame. Our study provides not only a multi-instance extension to CL and but also an MRI insight into the impact of mathematical studying on brain development.

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Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning

Cited by 35 publications

References 38 publications

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Male pelvic multi‐organ segmentation on transrectal ultrasound using anchor‐free mask CNN

An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

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