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Purpose Quantification of body tissue composition is important for research and clinical purposes, given the association between the presence and severity of several disease conditions, such as the incidence of cardiovascular and metabolic disorders, survival after chemotherapy, etc., with the quantity and quality of body tissue composition. In this work, we aim to automatically segment four key body tissues of interest, namely subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, and skeletal structures from body-torso-wide low-dose computed tomography (CT) images. Method Based on the idea of residual Encoder-Decoder architecture, a novel neural network design named ABCNet is proposed. The proposed system makes full use of multiscale features from four resolution levels to improve the segmentation accuracy. This network is built on a uniform convolutional unit and its derived units, which makes the ABCNet easy to implement. Several parameter compression methods, including Bottleneck, linear increasing feature maps in Dense Blocks, and memory-efficient techniques, are employed to lighten the network while making it deeper. The strategy of dynamic soft Dice loss is introduced to optimize the network in coarse-to-fine tuning. The proposed segmentation algorithm is accurate, robust, and very efficient in terms of both time and memory. Results A dataset composed of 38 low-dose unenhanced CT images, with 25 male and 13 female subjects in the age range 31-83 yr and ranging from normal to overweight to obese, is utilized to evaluate ABCNet. We compare four state-of-the-art methods including DeepMedic, 3D U-Net, V-Net, Dense V-Net, against ABCNet on this dataset. We employ a shuffle-split fivefold cross-validation strategy: In each experimental group, 18, 5, and 15 CT images are randomly selected out of 38 CT image sets for training, validation, and testing, respectively. The commonly used evaluation metrics - precision, recall, and F1-score (or Dice) - are employed to measure the segmentation quality. The results show that ABCNet achieves superior performance in accuracy of segmenting body tissues from body-torso-wide low-dose CT images compared to other state-of-the-art methods, reaching 92-98% in common accuracy metrics such as F1-score. ABCNet is also time-efficient and memory-efficient. It costs about 18 h to train and an average of 12 sec to segment four tissue components from a body-torso-wide CT image, on an ordinary desktop with a single ordinary GPU. Conclusions Motivated by applications in body tissue composition quantification on large population groups, our goal in this paper was to create an efficient and accurate body tissue segmentation method for use on body-torso-wide CT images. The proposed ABCNet achieves peak performance in both accuracy and efficiency that seems hard to improve any more. The experiments performed demonstrate that ABCNet can be run on an ordinary desktop with a single ordinary GPU, with practical times for both training and testing, and achieves superior accuracy compared to other state-of-the-art segmentation methods for the task of body tissue composition analysis from low-dose CT images.

Permittivity is critical for analyzing electromagnetic propagation in the substrate. The differential phase method is desirable to measure the permittivity of the substrate. The actual measurement is usually required to be started at a low frequency to avoid the S21-phase ambiguity problem. To get rid of this problem, the unwrapped S21-phase is calculated accurately at any measurement frequency range. In the classical method of calculating the real permittivity from the effective permittivity, the calculation error increases gradually with increasing frequency, especially in an ultra-wide frequency range. To effectively reduce the error, the ratio of the calculated and real permittivity is used in the permittivity correction, which is a function with respect to frequency, dimension, and permittivity of the microstrip line. A two-step fitting process is proposed to reduce the dimension of the ratio function from three-dimension to two-dimension. Furthermore, a quick-press test fixture is designed for a more convenient measurement, which is characterized by no soldering requirement and ultra-wide frequency range. Compared with the stripline resonant method, in the proposed method, the maximum deviations of the four substrates are 0.035, −0.04, 0.07, and 0.13, in the frequency range of 1–40 GHz.