Identification of the left ventricle endocardial border on two-dimensional ultrasound images using the convolutional neural network Unet

Zyuzin, Vasily; Sergey, Porshnev; Mukhtarov, A. A.; Chumarnaya, Tatiana; Solovyova, Olga; Bobkova, A S; Myasnikov, Vladislav

doi:10.1109/usbereit.2018.8384554

Cited by 24 publications

(9 citation statements)

References 5 publications

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“…Quite often, the deep learning techniques are combined with post-processing or other traditional techniques to boost the classification performance. In ultrasound images, both patch-based approach (Kaizhi et al 2014;Gustavo et al 2012;Smistad et al 2017;Lekadir et al 2017;Feng et al 2018;Jang et al 2017;Patra and Alison 2020;Zhu et al 2017;Ravishankar et al 2016;Zyuzin et al 2018;Jabbar et al 2016;Mishra et al 2018) and FCN Yap 2017;Oktay et al 2018;Milletari et al 2017;Sundaresan et al 2017;Wang et al 2019;Chiang et al 2019;Azzopardi et al 2020;Zhang et al 2020;Fujioka et al 2019;Liao et al 2019;Xing et al 2020;Andreassen et al 2019) are applied in various applications. In , Liu et al (2017a), the CNN was used with shape modeling to achieve better performance.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Ultrasound tissue classification: a review

et al. 2020

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Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

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Section: Deep Learning Approachesmentioning

confidence: 99%

Ultrasound tissue classification: a review

et al. 2020

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“…Here, max I * represents the maximum value of image intensities, N represents the total number of pixels, and i represents pixel index. PSNR can be normalized as: (8) where I * t represents a prediction of the t th frame and I t is the corresponding ground truth. The terms min PSNR and max PSNR are the minimum and maximum values of the PSNR in every frame of each test video.…”

Section: B Regular Scoresmentioning

confidence: 99%

“…A novel spatio-temporal U-Net for frame prediction is proposed. The new framework combines the benefits of U-Nets in representing spatial information [8] with the capabilities of ConvLSTM for modeling temporal motion data, which makes it more suitable for modeling image sequences. 2.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Unity Networking for Video Anomaly Detection

Yuan-hu

Liu

et al. 2019

IEEE Access

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Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio-temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.

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“…While deep learning (DL) algorithms have achieved remarkable success in various computer vision applications (segmentation, classification, detections, etc.) in a wide range of fields from medical images [14,15], scene understandings [13] or autonomous driving [1], we only find a few references yet for application of DL methods in architecture or cultural heritage documentation. In addition, existing methods [3,6] use deep learning rather for classification of the available images of the architectural heritage, instead of segmentation and feature extraction for detailed analysis.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

Ibrahim

Nagy

Benedek

2019

Lecture Notes in Computer Science

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Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%.

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Identification of the left ventricle endocardial border on two-dimensional ultrasound images using the convolutional neural network Unet

Cited by 24 publications

References 5 publications

Ultrasound tissue classification: a review

Ultrasound tissue classification: a review

Spatio-Temporal Unity Networking for Video Anomaly Detection

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

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