CNN‐Based Medical Ultrasound Image Quality Assessment

Zhang, Siyuan; Wang, Yifan; Jiang, Jinbo; Dong, Jingxian; Yi, Weiwei; Hou, Wenguang

doi:10.1155/2021/9938367

Cited by 18 publications

(18 citation statements)

References 28 publications

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“…This option for evaluation of video frames was opted to better understand the performance of each interpolated scan conversion algorithm, or else understand why an image or frame quality was bad or good at a specific time, in ultrasound systems. Here, it is important to note that, in the past, many attempts were done to develop methods to assess the quality of ultrasound imaging systems automatically or objectively [21], [22], [23]. In the recent past, the author introduced an index for image interpolation quality assessment as a preliminary step to a suitable method for image quality assessment in ultrasound imagingonly focusing on undesirable artefacts, known as aliasing [24].…”

Section: B Automatic / Objective Evaluation Results (Natural Images)mentioning

confidence: 99%

Stochastic Rounding for Image Interpolation and Scan Conversion

Rukundo¹,

Schmidt²

2022

IJACSA

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The stochastic rounding (SR) function is proposed to evaluate and demonstrate the effects of stochastically rounding row and column subscripts in image interpolation and scan conversion. The proposed SR function is based on a pseudorandom number, enabling the pseudorandom rounding up or down any non-integer row and column subscripts. Also, the SR function exceptionally enables rounding up any possible cases of subscript inputs that are inferior to a pseudorandom number. The algorithm of interest is the nearest-neighbor interpolation (NNI) which is traditionally based on the deterministic rounding (DR) function. Experimental simulation results are provided to demonstrate the performance of NNI-SR and NNI-DR algorithms before and after applying smoothing and sharpening filters of interest. Additional results are also provided to demonstrate the performance of NNI-SR and NNI-DR interpolated scan conversion algorithms in cardiac ultrasound videos.

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Section: B Automatic / Objective Evaluation Results (Natural Images)mentioning

confidence: 99%

Stochastic Rounding for Image Interpolation and Scan Conversion

Rukundo¹,

Schmidt²

2022

IJACSA

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“…This method, which requires only a single image and allows the calculation of uncertainties, is considered in this work. The approach is one of an increasing number of deep learning-based methods in the field of medical IQA [7][8][9][10][11]. Unfortunately, neural networks suffer from a lack of explainability due to their black-box nature [12], which is of particular relevance in safety-critical areas such as medicine.…”

Section: Introductionmentioning

confidence: 99%

Explainability for deep learning in mammography image quality assessment

Amanova

Martin

Elster

2022

Mach. Learn.: Sci. Technol.

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The application of deep learning has recently been proposed for the assessment of image quality in mammography. It was demonstrated in a proof-of-principle study that the proposed approach can be more efficient than currently applied automated conventional methods. However, in contrast to conventional methods, the deep learning approach has a black-box nature and, before it can be recommended for the routine use, it must be understood more thoroughly. For this purpose, we propose and apply a new explainability method: the oriented, modified integrated gradients (OMIG) method. The design of this method is inspired by the integrated gradients method but adapted considerably to the use case at hand. To further enhance this method, an upsampling technique is developed that produces high-resolution explainability maps for the downsampled data used by the deep learning approach. Comparison with established explainability methods demonstrates that the proposed approach yields substantially more expressive and informative results for our specific use case. Application of the proposed explainability approach generally confirms the validity of the considered deep learning-based mammography image quality assessment method. Specifically, it is demonstrated that the predicted image quality is based on a meaningful mapping that makes successful use of certain geometric structures of the images. In addition, the novel explainability method helps us to identify the parts of the employed phantom that have the largest impact on the predicted image quality, and to shed some light on cases in which the trained neural networks fail to work as expected. While tailored to assess a specific approach from deep learning for mammography image quality assessment, the proposed explainability method could also become relevant in other, similar deep learning applications based on high-dimensional images.

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“…Deep leaning techniques were proven efficient in solving multiple automation problems [13][14][15] in a variety of fields including medicine [16][17][18], security [19,20], management [21][22][23][24], and Internet of things [25]. With the proliferation of vision-based deep-learning techniques [26][27][28][29], significant advancements have been made in the generation of potential grasps.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.

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CNN‐Based Medical Ultrasound Image Quality Assessment

Cited by 18 publications

References 28 publications

Stochastic Rounding for Image Interpolation and Scan Conversion

Stochastic Rounding for Image Interpolation and Scan Conversion

Explainability for deep learning in mammography image quality assessment

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

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