Automatic Measurement of Spine Curvature on 3-D Ultrasound Volume Projection Image With Phase Features

Zhou, Guang-Quan; Jiang, Weiwei; Lai, Ka-Lee; Zheng, Yongping

doi:10.1109/tmi.2017.2674681

Cited by 59 publications

(27 citation statements)

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“…For decades, ultrasound image has been extensively applied in the detection of different diseases because of its high safety and high efficiency [ 1 – 3 ], such as the breast cancer, the liver cancer, the gastroenteric disease [ 4 ], the cardiovascular diseases [ 5 ], spine curvature [ 6 ], and the muscle disease [ 7 , 8 ]. However, it requires years of experience and training to read ultrasound image.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

161

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The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

161

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“…TI-RADS points are in the range from 0 to 7, respectively, expressed as ''benign'', ''not suspicious'', ''mildly suspicious'', ''moderately suspicious'' and ''highly suspicious'', are used to rank the risk of a malignant thyroid nodule. Besides, ultrasound image is also widely used in the detection of various diseases, such as the breast cancer [11]- [13], the liver cancer [14], the gastrointestinal disease [15], the cardiovascular diseases [16], spine curvature [17], and the muscle disease [18], [19]. These medical CADs based on ultrasound images have achieved excellent results and have good inspiration for our research.…”

Section: A Thyroid Nodule Ultrasound Diagnosesmentioning

confidence: 91%

Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network

et al. 2020

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With the increasing cases of thyroid malignant tumors, the diagnosis of thyroid nodule has attracted more and more attention. Deep learning has achieved promising results in computer-aided diagnosis due to the advantages of obtaining high-dimensional features. In this paper, we proposed a hybrid multi-branch convolutional neural network based on feature cropping method for feature extraction and classification of thyroid nodule ultrasound images. Firstly, we designed a backbone convolutional neural network to extract shared feature maps and a classification network as global branch. Next, we added a feature cropping branch in the network to perform multi-cropping on batch feature maps, to reduce the impact on classification caused by the similarity of local features between benign and malignant thyroid nodule images. Finally, based on softmax predictions of different branch feature maps, we utilize a weighted cross-entropy loss function to train our proposed binary-classification network. Experimental results show that our proposed method has achieved 96.13% accuracy, 93.24% precision, 97.18% recall, and 95.17% F1-measure in public dataset and local dataset, outperforming other models. INDEX TERMS Thyroid nodule classification, ultrasound image, hybrid multi-branch network, feature cropping, weighted loss function.

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“…This method is useful for the diagnosis of osteoporosis. Zhou et al [1] developed an automated measurement of the spine curvature from 3D Ultrasound Imaging. The method is useful for the diagnosis of scoliosis.…”

Section: Related Workmentioning

confidence: 99%

“…Two dimensional (2D) Ultrasound (US) is a standard modality in medical imaging [1][2][3][4][5]. It has several advantages.…”

Section: Introductionmentioning

confidence: 99%

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Spinal Cord Segmentation in Ultrasound Medical Imagery

et al. 2020

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In this paper, we study and evaluate the task of semantic segmentation of the spinal cord in ultrasound medical imagery. This task is useful for neurosurgeons to analyze the spinal cord movement during and after the laminectomy surgical operation. Laminectomy is performed on patients that suffer from an abnormal pressure made on the spinal cord. The surgeon operates by cutting the bones of the laminae and the intervening ligaments to relieve this pressure. During the surgery, ultrasound waves can pass through the laminectomy area to give real-time exploitable images of the spinal cord. The surgeon uses them to confirm spinal cord decompression or, occasionally, to assess a tumor adjacent to the spinal cord. The Freely pulsating spinal cord is a sign of adequate decompression. To evaluate the semantic segmentation approaches chosen in this study, we constructed two datasets using images collected from 10 different patients performing the laminectomy surgery. We found that the best solution for this task is Fully Convolutional DenseNets if the spinal cord is already in the train set. If the spinal cord does not exist in the train set, U-Net is the best. We also studied the effect of integrating inside both models some deep learning components like Atrous Spatial Pyramid Pooling (ASPP) and Depthwise Separable Convolution (DSC). We added a post-processing step and detailed the configurations to set for both models.

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Automatic Measurement of Spine Curvature on 3-D Ultrasound Volume Projection Image With Phase Features

Cited by 59 publications

References 50 publications

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network

Spinal Cord Segmentation in Ultrasound Medical Imagery

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