Breast ultrasound region of interest detection and lesion localisation

Yap, Moi Hoon; Goyal, Manu; Osman, Fatima; Martí, Robert; Denton, Erika R. E.; Juette, Arne; Zwiggelaar, Reyer

doi:10.1016/j.artmed.2020.101880

Cited by 90 publications

(68 citation statements)

References 28 publications

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“…The use of ultrasound imaging to detect breast lesions is considered to be an important step in a computer-aided diagnosis system. 107 ABUS can combine deep learning and convolutional neural network (CNN) to distinguish benign and malignant breast masses. 108 Vakanski et al 109 proposed a breast tumor segmentation method in ultrasound images that combines visual saliency and deep learning models.…”

Section: Image Guided Positioning Technologymentioning

confidence: 99%

Breast intervention surgery robot under image navigation: A review

Zhang

et al. 2021

Advances in Mechanical Engineering

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In recent years, breast cancer incidence has increased year by year, which makes research on treatment methods critical. Intervention surgery is a new development in breast cancer treatment. However, the operation is complicated and lasts for a long time. The doctor’s operation can no longer meet clinical needs. The breast intervention robot has the characteristics of minimal invasiveness, operability, and high flexibility so that it can compensate for the shortcomings of traditional interventional surgery. The combination of the robotic system and medical imaging technology is an important means to determine the lesion location and plan the puncture path. This article mainly reviews the breast intervention robot under image navigation. Based on existing literature, image navigation methods are divided into MRI, ultrasound, and CT for the introduction. First, we summarized the principles and technologies associated with materials, sensors, and actuators used in the MRI-guided breast intervention robot. In particular, the actuation is analyzed and compared in detail. Subsequently, the ultrasound-guided and CT-guided breast intervention robots are introduced. After that, we discuss the image-guided positioning technology. Finally, we summarize the research progress and trends in the future development of breast intervention robots under image navigation.

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Section: Image Guided Positioning Technologymentioning

confidence: 99%

Breast intervention surgery robot under image navigation: A review

Zhang

et al. 2021

Advances in Mechanical Engineering

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“…Breast lesion segmentation remains an unsolved problem as a result of these difficulties [ 15 , 16 , 17 ]. Existing studies lack vigor, intensity inhomogeneity, artifact removal, and precise lesion segmentation [ 15 , 16 , 18 , 19 ]. Because of the deep convolutional process, which extracts rich feature vectors, deep learning-based approaches for semantic segmentation and classification have gained popularity [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

et al. 2021

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Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

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“…It is widely used in target detection and recognition in natural images (9)(10)(11)(12) and also shows high efficiency in clinical applications. It has been used in the region of interest detection and lesion localization on medical images, such as ultrasound images, X-ray images, and CT images (13)(14)(15). Sa et al (13) applied a fine-tuned Faster-RCNN trained on natural images to identify landmark points in lateral lumbar X-ray images and demonstrated that using very small annotated clinical datasets can also achieve great accuracy.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Feature Extraction of DSA Video and Its Application in AVM Diagnosis

Shi

Xiao

et al. 2021

Front. Neurol.

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Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

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Breast ultrasound region of interest detection and lesion localisation

Cited by 90 publications

References 28 publications

Breast intervention surgery robot under image navigation: A review

Breast intervention surgery robot under image navigation: A review

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Temporal-Spatial Feature Extraction of DSA Video and Its Application in AVM Diagnosis

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