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.
* Automatic US shadow detection Acoustic shadows have a significant impact on US image quality, and thus a serious effect on robustness and accuracy of image processing methods. In clinical literature, US artifacts including shadows have been well studied and reviewed @cite_17 @cite_24 @cite_33 . However, the shadow problem is not well covered in automated US image analysis literature. Anatomically estimating acoustic shadow has rarely been the focus within the medical image analysis community as it is a challenging task.
@cite_24 @cite_33 @cite_17
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Lung ultrasound can be routinely performed at the bedside by intensive care unit physicians and may provide accurate information on lung status with diagnostic and therapeutic relevance. This article reviews the performance of bedside lung ultrasound for diagnosing pleural effusion, pneumothorax, alveolar-interstitial syndrome, lung consolidation, pulmonary abscess and lung recruitment derecruitment in critically ill patients with acute lung injury. AbstractUltrasound image segmentation deals with delineating the boundaries of structures, as a step towards semi-automated or fully automated measurement of dimensions or for characterizing tissue regions. Ultrasound tissue characterization (UTC) is driven by knowledge of the physics of ultrasound and its interactions with biological tissue, and has traditionally used signal modelling and analysis to characterize and differentiate between healthy and diseased tissue. Thus, both aim to enhance the capabilities of ultrasound as a quantitative tool in clinical medicine, and the two end goals can be the same, namely to characterize the health of tissue. This article reviews both research topics, and finds that the two fields are becoming more tightly coupled, even though there are key challenges to overcome in each area, influenced by factors such as more open software-based ultrasound system architectures, increased computational power, and advances in imaging transducer design.