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Our experience demonstrated that laparoscopic caudate lobectomy is safe and feasible in selected patients with malignancy in the caudate lobe.

Quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT/CT at 20 min, sialagogue stimulation, and SPECT at 40 min post-injection of Tc-99m pertechnetate (555 MBq). The 40-min SPECT was reconstructed using the 20-min CT after misregistration correction. Manual salivary gland segmentation for %injected dose (%ID) by human experts proved highly reproducible, but took 15 min per scan. An automatic salivary segmentation method was developed using a modified 3D U-Net for end-to-end learning from the human experts (n = 333). The automatic segmentation performed comparably with human experts in voxel-wise comparison (mean Dice similarity coefficient of 0.81 for parotid and 0.79 for submandibular, respectively) and gland %ID correlation (R2 = 0.93 parotid, R2 = 0.95 submandibular) with an operating time less than 1 min. The algorithm generated results that were comparable to the reference data. In conclusion, with the aid of a CNN, we developed a quantitative salivary gland SPECT/CT protocol feasible for clinical applications. The method saves analysis time and manual effort while reducing patients’ radiation exposure.

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This chapter reports an annual production of Spirulina (Arthrospira) maxima in Ansan, South Korea (37.287°N, 126.833°E) with temperate four seasons climate for testing industrial application. Construction on pilot plant of semi-open raceway system (ORS) with each 20 ton culture volume has been established in early 2011 based on building information modeling (BIM). An optimized design of pilot culture system for microalgae scale-up culture in temperate area and details of culture was presented. In scale-up trials using two ORSs, the strain displayed satisfactory annual growth under batch condition. In an annual trial, average biomass concentration was recorded at 0.99 ± 0.16 g/L, which showed stable productivity in a year. Maximum concentration was estimated at 1.418 ± 0.09 g/L in August, while minimum production was estimated at 0.597 ± 0.05 g/L in October. Despite insufficient solar radiation and nutrients, ORS was favorable for S. maxima production. The technical strategies contribute to the annual production of S. maxima in this region: controlling the culture temperature, reducing production cost, and retrospective climatic data-based BIM construction of the greenhouse. Consequently, pilot production of S. maxima was feasible in Korean climates, a region previously thought to be outside its geographic limits.

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Purpose Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid. Methods Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification. Results The synthetic μ-map demonstrated a strong correlation (R2 = 0.972) and minimum error (mean square error = 0.936 × 10−4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of − 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29). Conclusion CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning.

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