Cervical Extension of the Normal Thymus in Children and Adolescents: Sonographic Features and Prevalence.

OBJECTIVE: This study aims to confirm the prevalence of incidental cervical extension of normal thymus in children and adolescents undergoing neck ultrasound and describe the ultrasound appearance to minimize future misdiagnosis. MATERIALS AND METHODS: This retrospective study was conducted in a single institution. Thyroid and lower neck ultrasound images of the consecutive pediatric subjects between January 1, 2011 and September 30, 2017 were independently reviewed by 2 radiologists for the presence of cervical thymus. When identified on sonographic images, cervical thymus was described on the basis of echogenicity, location, and shape. RESULTS: In 278 consecutive cases, the 2 reviewers identified 105 (37.8%) and 103 (37.1%) cases respectively as having sonographically visible tissue in the expected location of cervical extension of the thymus. The internal echotexture was variable with 38.1% of cases being hypoechoic, 37.1% mixed, and 24.8% hyperechoic. Cervical extension of the thymus was most commonly (65.0%) to the left of the trachea or (30.9%) bilateral/anterior to the trachea; isolated right paratracheal thymus was uncommon. Thymic shape was variable: quadrilateral (30.9%), oval (29.9%), triangular (25.8%), and other (13.4%). The logistic regression model including age, gender, and BMI z-scores showed that, when controlled for sex and BMI z-scores, younger age was a predictor for the presence of cervical thymic extension (p < .001). CONCLUSION: Cervical thymic extension is sonographically visible as a soft tissue mass of variable appearance in about a third of children and adolescents undergoing neck ultrasonography with decreasing prevalence with age. Sonographically visible cervical thymic tissue is more common in younger patients.

Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation.

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting the variety of the receptive fields. Second, the network has a symmetric structure where the encoder and the decoder paths have similar numbers of channels. We explored two novel revisions: a stacked dilated operation that outputs feature maps from multi-scale receptive fields to replace the consecutive convolutions; an asymmetric architecture with fewer channels in the decoder path. Two novel models were developed: U-Net using the stacked dilated operation (SDU-Net) and asymmetric SDU-Net (ASDU-Net). We used both publicly available and private datasets to assess the efficacy of the proposed models. Extensive experiments confirmed SDU-Net outperformed or achieved performance similar to the state-of-the-art while using fewer parameters (40% of U-Net). ASDU-Net further reduced the model parameters to 20% of U-Net with performance comparable to SDU-Net. In conclusion, the stacked dilated operation and the asymmetric structure are promising for improving the performance of U-Net and its variants.

HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy.

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles' diameter is usually in the range of 2-10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

Development of a generalized model for kidney depth estimation in the Chinese population: A multi-center study.

PURPOSE: To establish an accurate and reliable equation for kidney depth estimation in adult patients from different Chinese geographical regions. METHOD: This multicenter study enrolled Eastern Asian Chinese patients with abdominal PET/CT scans at 26 imaging centers from six macro-regions across China in 3 years. Age, gender, height, weight, primary disease and its extent on PET scans of the participants were collected as potential predictive factors. Kidney depth on CT, defined as the average of the vertical distances from the posterior skin to the farthest anterior and closest posterior surfaces of each kidney, was measured as the standard reference. The new kidney depth model was constructed using a multiple regression model, and its performance was compared to those of three established models by computing the absolute value of estimation errors in comparison with CT-measured kidney depth. RESULTS: A total of 2502 patients were enrolled and classified into training (n=1653) and testing (n = 849) subsets. In the training subset, two kidney depth models were constructed: Left (cm): 0.013×age+0.117×gender-0.044×height+0.087×weight+7.951, Right (cm): 0.005×age+0.013×gender-0.035×height+0.082×weight+7.266 (weight: kg, height: cm, gender = 0 if female, 1 if male). In the testing subset, one-way analysis of variance showed that the estimation errors of the new models did not significantly differ among the 6 regions. Bland-Altman analysis determined that new equations had lower estimated biases (left: 0.039 cm, right: 0.018 cm) compared with other existing models. CONCLUSION: The new equations were highly accurate for kidney depth estimation in adults from all over China, with lower estimation errors compared to other established models.