KEYWORDS: Ultrasonography, Statistical analysis, Education and training, Deep learning, Data modeling, Tissues, Point spread functions, Measurement uncertainty, Backscatter, Scattering
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has been found to be a reliable biomarker to detect different abnormalities. The homodyned K-distribution (HK-distribution) is a model for the probability density function of the ultrasound echo amplitude that can model different scattering scenarios but requires a large number of samples to be estimated reliably. Parametric images of HK-distribution parameters can be formed by dividing the envelope data into small overlapping patches and estimating parameters within the patches independently. This approach imposes two limiting constraints: the HK-distribution parameters are assumed to be constant within each patch, and each patch requires enough independent samples. In order to mitigate those problems, we employ a deep learning approach to estimate parametric images of scatterer number density (related to HK-distribution shape parameter) without patching. Furthermore, an uncertainty map of the network’s prediction is quantified to provide insight about the confidence of the network about the estimated HK parameter values.
Breast cancer is the second leading cause of cancer death in women worldwide. Ultrasound is one of the most used tools for image-based assessment of this disease, it helps to discriminate between benign or malignant masses, however, it depends largely on the radiologists experience. In recent years there has been a special interest to develop automatic segmentation systems (radiologist independent) for ultrasound images. The main challenges to get a clear ultrasound segmentation are: speckle noise, low contrast, blurred edges, shadows, etc. In this work, we use the Small Tumor Aware Network (STAN) architecture to automatically segment the images and we validate the network using eleven metrics on five combined breast ultrasound datasets. Dice’s coefficient indicates that predicted segmentations are 84% similar to the ground truth while high recall, specificity and accuracy results are being obtained. We use this information to calculate the tortuosity to differentiate between malignant and benign lesions, the Wilcoxon test results are p < 0.05 with a z = 2.73.
KEYWORDS: Ultrasonography, Scattering, Signal to noise ratio, Breast cancer, Tissues, Breast, Data acquisition, Image processing, Statistical analysis, Signal processing
In this study we present a preliminary evaluation of the inter-operator (InterOp) and intra-operator (IntraOp) variability of Quantitative Ultrasound features based on first-order speckle statistics used in breast cancer characterization. Ultrasound echo signals from ten patients with biopsy-confirmed invasive ductal carcinomas were acquired in vivo in the radial and antiradial planes with a commercial ultrasound system using a linear array transducer. Each patient was scanned by three radiologists, each of which performed three acquisitions allowing the patient to reposition in between acquisitions. Parametric images of six QUS features obtained from the first order statistics of the speckle pattern of ultrasound images were computed, and the mean feature value within the lesion boundary was compared between pairs of images from different radiologists or acquisitions from the same radiologist. In general, the InterOp variability was 1.2 times larger than the IntraOp variability. These differences were not significant in the radial plane. In addition, features with similar InterOp and IntraOp variability were the ones with the largest overall variability.
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