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.
Some studies have utilized acoustic shadow detection as additional information in their pipeline for other US image processing tasks. @cite_11 combined acoustic shadow detection for the characterization of dense calcium tissue in intravascular US virtual histology, and @cite_8 automatically and simultaneously segment vertebrae, spinous process and acoustic shadow in US images for a better assessment of scoliosis progression. In these applications, acoustic shadow detection is task-specific, and is mainly based on heuristic image intensity features as well as special anatomical constraints.
@cite_8 @cite_11
2305452864 2205506937
Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84 , 92 and 91 were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression. Graphical abstractDisplay Omitted HighlightsTexture descriptors and state-of-the art features allowed accurate segmentation.The features were optimized for vertebral region discrimination in ultrasound.Regularization accounts for geometrical properties of vertebral ultrasound images. We enhance intravascular ultrasound virtual histology (VH) tissue characterization by fully automatic quantification of the acoustic shadow behind calcified plaque. VH is unable to characterize atherosclerosis located behind calcifications. In this study, the quantified acoustic shadows are considered calcified to approximate the real dense calcium (DC) plaque volume. In total, 57 patients with 108 coronary lesions were included. A novel post-processing step is applied on the VH images to quantify the acoustic shadow and enhance the VH results. The VH and enhanced VH results are compared to quantitative computed tomography angiography (QTA) plaque characterization as reference standard. The correlation of the plaque types between enhanced VH and QTA differs significantly from the correlation with unenhanced VH. For DC, the correlation improved from 0.733 to 0.818. Instead of an underestimation of DC in VH with a bias of 8.5 mm3, there was a smaller overestimation of 1.1 mm3 in the enhanced VH. Although tissue characterization within the acoustic shadow in VH is difficult, the novel algorithm improved the DC tissue characterization. This algorithm contributes to accurate assessment of calcium on VH and could be applied in clinical studies.