Segmentation and classification of knee joint ultrasonic image via deep learning

Long, Zhili; Zhang, Xiaobing; Li, Cong; Jin, Niu; Wu, Xiaojun; Li, Zuohua

doi:10.1016/j.asoc.2020.106765

Cited by 18 publications

(4 citation statements)

References 25 publications

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“…There are multiple clinical settings in which application of ML models can aid MSUS. These include the assessment of synovial tissue (15,18), tendon (19,20), cartilage (21) and nerve identification (11,22,23). When examining these structures, a ML algorithm can perform either recognition or a diagnostic task.…”

Section: Ai-based Techniques For Ultrasound Imagingmentioning

confidence: 99%

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Dinescu,

Stoica,

Bita

et al. 2023

Front. Med.

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Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner’s experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice.

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Section: Ai-based Techniques For Ultrasound Imagingmentioning

confidence: 99%

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Dinescu,

Stoica,

Bita

et al. 2023

Front. Med.

View full text Add to dashboard Cite

show abstract

“…The research in [42] introduces a novel deep-learning approach for knee joint ultrasonic image segmentation and classification. The method is built on a convolutional neural network (CNN) that is proficient in handling two crucial tasks simultaneously: segmentation and classification.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

LAGHMATI,

HICHAM,

CHERRADI

et al. 2023

IJACSA

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Breast cancer (BC) is one of the most prevailing and life-threatening types of cancer impacting women worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages the Breast Ultrasound Images BUSI dataset to develop two variations of a segmentation model using the Attention U-Net architecture. In this study, we trained the Attention3 U-Net and the Attention4 U-net on the BUSI dataset, consisting of normal, benign, and malignant breast lesions. We evaluated the model's performance based on standard segmentation metrics such as the Dice coefficient and Intersection over Union (IoU). The results demonstrate the effectiveness of the Attention U-Net in accurately segmenting breast lesions, with high overall performance, indicating agreement between predicted and ground truth masks. The successful application of the Attention U-Net to the BUSI dataset holds promise for improving breast cancer diagnosis and treatment. It highlights the potential of deep learning in medical image analysis, paving the way for more efficient and reliable diagnostic tools in breast cancer management.

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“…In the clinical diagnosis of fetal development during pregnancy, ultrasound images help doctors better understand the situation, and provide sufficient basis for the judgment whether the fetal development meets the standard or is abnormal [1][2][3][4][5][6]. In order to make an in-depth analysis of the target in the uterus, it is necessary to segment the target from the ultrasound image [7][8][9][10][11][12]. In order to compare the changes of fetal growth index in different periods, it is also necessary to register the fetal development ultrasound images in different periods, even if the corresponding structures of different ultrasound images are consistent in space [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

Zhang¹,

Cao²

2023

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Image segmentation and registration are the premise of ultrasonic image analysis. The key of computer-aided clinical diagnosis of fetal development is to improve the accuracy and speed of ultrasound image segmentation and registration, which is worth further discussion. As for the existing research results, problems still remain in the accuracy and effect of segmentation and registration. Therefore, this paper studied the fetal development ultrasound image segmentation and registration based on deep learning. In Chapter 2, the paper designed a convolution module, dividing the feature information generation process into two steps. The introduced self-tuning lightweight segmentation module and channel attention module were used to enhance the expression ability of features and improve the segmentation performance of the Convolutional Neural Network (CNN), respectively. In Chapter 3, this paper constructed a full-CNN model based on joint training to perform nonrigid registration of fetal development ultrasound images, which reduced the computational complexity of the model. The experimental results verified the effectiveness of the model.

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Segmentation and classification of knee joint ultrasonic image via deep learning

Cited by 18 publications

References 25 publications

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

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