A semi-automated 3-D annotation method for breast ultrasound imaging: system development and feasibility study on phantoms

A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography

BACKGROUND

Breast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024.

OBJECTIVE

The prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user's level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner's expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting.

METHOD

This work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task.

RESULTS

The model achieved the best results using the softmax classifier, with an accuracy of over 95%.

CONCLUSION

Computer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions.

A real-time freehand 3D ultrasound imaging method for scoliosis assessment

Real‐time 3D ultrasound has gained popularity in many fields because it can provide interactive feedback to help acquire high‐quality images or to conduct timely diagnosis. However, no comprehensive study has been reported on such an imaging method for scoliosis evaluation due to the complexity of this application. Meanwhile, the use of radiation‐free assessment of scoliosis is becoming increasingly popular. This study developed a real‐time 3D ultrasound imaging method for scoliosis assessment based on an incremental imaging method. In vivo experiments involving 36 patients with scoliosis were performed to test the performance of the proposed method. This new imaging method achieved a mean incremental frame rate of 82.7 ± 11.0 frames/s. The high repeatability of the intra‐operator test (intraclass correlation coefficient [ICC] = 0.92) and inter‐operator test (ICC = 0.91) demonstrated that the new method was very reliable. The result of spinous process angles obtained by the new method was linearly correlated (y = 0.97x, R² = 0.88) with that obtained by conventional 3D reconstruction. These results suggested that the newly developed imaging method can provide real‐time ultrasound imaging for scoliosis evaluation while preserving the comparative image quality of the conventional 3D reconstruction method.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Simple Summary

Breast cancer is one of the most common cancers among women globally. Early and accurate screening of breast tumours can improve survival. Ultrasound elastography is a non-invasive and non-ionizing imaging approach to characterize lesions for breast cancer screening, while machine learning techniques could improve the accuracy and reliability of computer-aided diagnosis. This review focuses on the state-of-the-art development and application of the machine learning model in breast tumour classification.

Abstract

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ≥80%, while only half of them attained acceptable specificity ≥95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Clinical Evaluation of a 3-D Automatic Annotation Method for Breast Ultrasound Imaging

The routine clinical breast ultrasound annotation method is limited by the time it consumes, inconsistency, inaccuracy and incomplete notation. A novel 3-D automatic annotation method for breast ultrasound imaging has been developed that uses a spatial sensor to track and record conventional B-mode scanning so as to provide more objective annotation. The aim of the study described here was to test the feasibility of the automatic annotation method in clinical breast ultrasound scanning. An ultrasound scanning procedure using the new method was established. The new method and the conventional manual annotation method were compared in 46 breast cancer patients (49 ± 12 y). The time used for scanning a patient was recorded and compared for the two methods. Intra-observer and inter-observer experiments were performed, and intra-class correlation coefficients (ICCs) were calculated to analyze system reproducibility. The results revealed that the new annotation method had an average scanning time 36 s (42.9%) less than that of the conventional method. There were high correlations between the results of the two annotation methods (r = 0.933, p < 0.0001 for distance; r = 0.995, p < 0.0001 for radial angle). Intra-observer and inter-observer reproducibility was excellent, with all ICCs > 0.92. The results indicated that the 3-D automatic annotation method is reliable for clinical breast ultrasound scanning and can greatly reduce scanning time. Although large-scale clinical studies are still needed, this work verified that the new annotation method has potential to be a valuable tool in breast ultrasound examination.

Freehand three-dimensional ultrasound system for assessment of scoliosis

Background/Objective

Standing radiograph with Cobb's method is routinely used to diagnose scoliosis, a medical condition defined as a lateral spine curvature > 10° with concordant vertebral rotation. However, radiation hazard and two-dimensional (2-D) viewing of 3-D anatomy restrict the application of radiograph in scoliosis examination.

Methods

In this study, a freehand 3-D ultrasound system was developed for the radiation-free assessment of scoliosis. Bony landmarks of the spine were manually extracted from a series of ultrasound images with their spatial information recorded to form a 3-D spine model for measuring its curvature. To validate its feasibility, in vivo measurements were conducted in 28 volunteers (age: 28.0 ± 13.0 years, 9 males and 19 females). A significant linear correlation (R² = 0.86; p < 0.001) was found between the spine curvatures as measured by Cobb's method and the 3-D ultrasound imaging with transverse process and superior articular process as landmarks. The intra- and interobserver tests indicated that the proposed method is repeatable.

Results

The 3-D ultrasound method using bony landmarks tended to underestimate the deformity, and a proper scaling is required. Nevertheless, this study demonstrated the feasibility of the freehand 3-D ultrasound system to assess scoliosis in the standing posture with the proposed methods and 3-D spine profile.

Conclusion

Further studies are required to understand the variations that exist between the ultrasound and radiograph results with a larger number of volunteers, and to demonstrate its potential clinical applications for monitoring of scoliosis patients. Through further clinical trials and development, the reported 3-D ultrasound imaging system can potentially be used for scoliosis mass screening and frequent monitoring of progress and treatment outcome because of its radiation-free and easy accessibility feature.

A novel breast ultrasound system for providing coronal images: system development and feasibility study

Breast ultrasound images along coronal plane contain important diagnosis information. However, conventional clinical 2D ultrasound cannot provide such images. In order to solve this problem, we developed a novel ultrasound system aimed at providing breast coronal images. In this system, a spatial sensor was fixed on an ultrasound probe to obtain the image spatial data. A narrow-band rendering method was used to form coronal images based on B-mode images and their corresponding spatial data. Software was developed for data acquisition, processing, rendering and visualization. In phantom experiments, 20 inclusions with different size (5-20 mm) were measured using this new system. The results obtained by the new method well correlated with those measured by a micrometer (y=1.0147x, R(2)=0.9927). The phantom tests also showed that this system had excellent intra- and inter-operator repeatability (ICC>0.995). Three subjects with breast lesions were scanned in vivo using this new system and a commercially available three-dimensional (3D) probe. The average scanning times for the two systems were 64 s and 74 s, respectively. The results revealed that this new method required shorter scanning time. The tumor sizes measured on the coronal plane provided by the new method were smaller by 5.6-11.9% in comparison with the results of the 3D probe. The phantom tests and preliminary subject tests indicated the feasibility of this system for clinical applications by providing additional information for clinical breast ultrasound diagnosis.