Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions is challenging because pixel-wise annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions, which is able to generate a dense shadow-focused confidence map. During training, a multi-task module for shadow segmentation is built to learn general shadow features according based image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is then established to extend the binary shadow segmentation to a reference confidence map. In addition, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This confidence estimation network is able to predict shadow confidence maps directly from input images during inference. We evaluate DICE, soft DICE, recall, precision, mean squared error and inter-class correlation to verify the effectiveness of our method. Our method outperforms the state-of-the-art qualitatively and quantitatively. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.
Identifying shadow regions in US images has been utilized as a preprocessing step for extracting valid image content and improving image analysis accuracy in some applications. @cite_21 has identified shadow regions by thresholding the accumulated intensity along each scanning beam line. Afterwards, these shadow regions have been masked out from US images for an US to magnetic resonance (MR) hepatic image registration. Instead of excluding shadow regions, @cite_2 focused on accurate attenuation estimation, and aimed to use attenuation properties for determination of the anatomical properties which can help diagnose diseases. @cite_2 proposed a hybrid attenuation estimation method that combines spectral difference and spectral shift methods to reduce the influence of local spectral noise and backscatter variations in Radio Frequency (RF) US data. To detect shadow regions in B-Mode scans directly and automatically, @cite_0 have introduced the probe's geometric information and statistically modelled the US B-Mode cone. Compared with previous statistical shadow detection methods such as @cite_21 , this method can automatically estimate the probe's geometry as well as other hyperparameters, and has shown improvements in 3D reconstruction, registration and tracking. However, this method can only detect a subset of deep' acoustic shadows because of the probe geometry-dependant sampling strategy.
@cite_0 @cite_21 @cite_2
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Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise, shadow-focused confidence map from weakly labelled, anatomically-focused images. Our method: (1) initializes potential shadow areas based on a classification task. (2) extends potential shadow areas using a GAN model. (3) adds intensity information to generate the final confidence map using a distance matrix. The proposed method accurately highlights the shadow areas in 2D ultrasound datasets comprising standard view planes as acquired during fet al screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement. This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load. In ultrasound images, acoustic shadows appear as regions of low signal intensity linked to boundaries with very high acoustic impedance differences. Acoustic shadows can be viewed either as informative features to detect lesions or calcifications, or as damageable artifacts for image processing tasks such as segmentation, registration or 3D reconstruction. In both cases, the detection of these acoustic shadows is useful. This paper proposes a new method to detect these shadows that combines a geometrical approach to estimate the B-scans shape, followed by a statistical test based on a dedicated modeling of ultrasound image statistics. Results demonstrate that the combined geometrical-statistical technique is more robust and yields better results than the previous statistical technique. Integration of regularization over time further improves robustness. Application of the procedure results in (1) improved 3D reconstructions with fewer artifacts, and (2) reduced mean registration error of tracked intraoperative brain ultrasound images. Abtract—Attenuation estimation methods for medical ultrasound are important because attenuation properties of soft tissue can be used to distinguish between benign and malignant tumors and to detect diffuse disease. The classical spectral shift method and the spectral difference method are the most commonly used methods for the estimation of the attenuation; however, they both have specific limitations. Classical spectral shift approaches for estimating ultrasonic attenuation are more sensitive to local spectral noise artifacts and have difficulty in compensating for diffraction effects because of beam focusing. Spectral difference approaches, on the other hand, fail to accurately estimate attenuation coefficient values at tissue boundaries that also possess variations in the backscatter. In this paper, we propose a hybrid attenuation estimation method that combines the advantages of the spectral difference and spectral shift methods to overcome their specific limitations. The proposed hybrid method initially uses the spectral difference approach to reduce the impact of system-dependent parameters including diffraction effects. The normalized power spectrum that includes variations because of backscatter changes is then filtered using a Gaussian filter centered at the transmit center frequency of the system. A spectral shift method, namely the spectral cross-correlation algorithm is then used to compute spectral shifts from these filtered power spectra to estimate the attenuation coefficient. Ultrasound simulation results demonstrate that the estimation accuracy of the hybrid method is better than the centroid downshift method (spectral shift method), in uniformly attenuating regions. In addition, this method is also stable at boundaries with variations in the backscatter when compared with the reference phantom method (spectral difference method). Experimental results using tissue-mimicking phantom also illustrate that the hybrid method is more robust and provides accurate attenuation estimates in both uniformly attenuating regions and across boundaries with backscatter variations. The proposed hybrid method preserves the advantages of both the spectral shift and spectral difference approaches while eliminating the disadvantages associated with each of these methods, thereby improving the accuracy and robustness of the attenuation estimation. (E-mail: hyungsukkim@wisc.edu) © 2008 World Federation for Ultrasound in Medicine & Biology.