Computer-aided Diagnosis in Breast Ultrasound

Cancer remains a leading cause of death in Taiwan, and the prevalence of breast cancer has increased in recent years. The early detection and diagnosis of breast cancer is the key to ensuring prompt treatment and a reduced death rate. Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer. The heterogeneity of breast cancers leads to an overlap in benign and malignant ultrasonography images, and US examinations are also operator dependent. Recently, computer-aided diagnosis (CAD) has become a major research topic in medical imaging and diagnosis. Technical advances such as tissue harmonic imaging, compound imaging, split screen imaging and extended field-of-view imaging, Doppler US, the use of intravenous contrast agents, elastography, and CAD systems have expanded the clinical application of breast US. Breast US CAD can be an efficient computerized model to provide a second opinion and avoid interobserver variation. Various breast US CAD systems have been developed using techniques which combine image texture extraction and a decision-making algorithm. However, the textural analysis is system dependent and can only be performed well using one specific US system. Recently, several researchers have demonstrated the use of such CAD systems with various US machines mainly for preprocessing techniques designed to homogenize textural features between systems. Morphology-based CAD systems used for the diagnosis of solid breast tumors have the advantage of being nearly independent of either the settings of US systems or different US machines. Future research on CAD systems should include pathologically specific tissue-related and hormonerelated conjecture, which could be applied to picture archiving and communication systems or teleradiology

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality

Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors

International audienceBreast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level de-scriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art

Building and validation of low-cost breast phantoms for interventional procedures

Breast cancer is one of the types of cancer with the highest incidence in female population. Current treatment for breast cancer is lumpectomy, a breast conserving tumor excision procedure based on localizing the tumor with the help of hook-wire needle placement. Although this constitutes the standard approach in clinical practice, these procedures do not ensure the complete removal of the lesion due to the demonstrated high rate of positive margins. Improvements in these techniques are needed in order to reduce the number of second interventions, which usually involve mastectomy. Here is where ultrasound-guided interventions with real-time position tracking find their place. The problem is that these techniques require a high level of expertise and they present long learning curves. Therefore, training is needed in order to get from these tools their highest potential and have a real impact in the life of patients. For this purpose, breast phantoms were manufactured using liquid vinyl in order to achieve a mammary mimicking tissue. Optimal manufacturing technique was determined based on a gold-standard (commercial phantom). CT and ultrasound imaging were used to assess the identification of lesions. In addition, manufactured breast phantoms were evaluated by an expert clinician and surgical navigation was tested. This was done with the purpose of validating the breast phantom as a training tool useful for improving the outcomes of these procedures. The results indicated that the optimized formula achieved for the manufacturing of low-cost breast phantoms was suitable for training the skillset required in the interventions related with breast cancer treatment.Ingeniería Biomédica (Plan 2010

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.info:eu-repo/semantics/publishedVersio

The potential role of MR based radiomic biomarkers in the characterization of focal testicular lesions

How to differentiate with MRI-based techniques testicular germ (TGCTs) and testicular non-germ cell tumors (TNGCTs) is still under debate and Radiomics may be the turning key. Our purpose is to investigate the performance of MRI-based Radiomics signatures for the preoperative prediction of testicular neoplasm histology. The aim is twofold: (i), differentiating TGCTs and TNGCTs status and (ii) differentiating seminomas (SGCTs) from non-seminomatous (NSGCTs). Forty-two patients with pathology-proven testicular neoplasms and referred for pre-treatment MRI, were retrospectively enrolled. Thirty-two out of 44 lesions were TGCTs. Twelve out of 44 were TNGCTs or other histologies. Two radiologists segmented the volume of interest on T2-weighted images. Approximately 500 imaging features were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was applied as method for variable selection. A linear model and a linear support vector machine (SVM) were trained with selected features to assess discrimination scores for the two endpoints. LASSO identified 3 features that were employed to build fivefold validated linear discriminant and linear SVM classifiers for the TGCT-TNGCT endpoint giving an overall accuracy of 89%. Four features were employed to build another SVM for the SGCT-SNGCT endpoint with an overall accuracy of 86%. The data obtained proved that T2-weighted-based Radiomics is a promising tool in the diagnostic workup of testicular neoplasms by discriminating germ cell from non-gem cell tumors, and seminomas from non-seminomas