05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Author: search.filters.author.Winter, MariusPublication Type: search.filters.UBSTyp.Zeitschriftenartikel

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    Ultraviolet photodetectors and readout based on a‐IGZO semiconductor technology
    (2023) Schellander, Yannick; Winter, Marius; Schamber, Maurice; Munkes, Fabian; Schalberger, Patrick; Kuebler, Harald; Pfau, Tilman; Fruehauf, Norbert
    In this work, real-time ultraviolet photodetectors are realized through metal–semiconductor–metal (MSM) structures. Amorphous indium gallium zinc oxide (a-IGZO) is used as semiconductor material and gold as metal electrodes. The readout of an individual sensor is implemented by a transimpedance amplifier (TIA) consisting of an all-enhancement a-IGZO thin-film transistor (TFT) operational amplifier and a switched capacitor (SC) as feedback resistance. The photosensor and the transimpedance amplifier are both manufactured on glass substrates. The measured photosensor possesses a high responsivity R, a low response time tRES, and a good noise equivalent power value NEP.
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    Age- and BMI-dependent psoas and gluteus muscle mass in 27,805 participants of the population-based German National Cohort (NAKO Gesundheitsstudie) : a deep-learning 3T MRI study
    (2026) Kiefer, Lena Sophie; Winter, Marius; Pappa, Sofia; Fischer, Marc; Küstner, Thomas; Diallo, Thierno D.; Calderón, Eduardo; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz
    Background/Objective: This study aimed to develop and validate an automated deep learning-based model for 3D segmentation and quantification of the psoas major and gluteus muscles at 3T MRI in a large cohort study and to analyze the distribution of findings as well as gender-, age-, and BMI-related differences. Methods: The study population consisted of 27,805 participants from the MR imaging subgroup of the population-based, longitudinal German National Cohort study. A deep learning segmentation model was trained, tested, and implemented to automatically quantify psoas major maximum cross-sectional area (CSApsoas) and gluteus volume (Vgluteus) on T1-weighted 3D VIBE DIXON sequences. Associations with gender, age, and BMI were assessed by linear regression. Results: The segmentation model demonstrated a high performance, with mean Dice coefficients of 0.92 for the psoas and 0.95 for the gluteus. Males showed higher total CSApsoas (males: 37.92 ± 5.80 cm2; females: 24.47 ± 3.65 cm2) and higher total Vgluteus (males: 3.384 ± 0.528 L; females: 2.386 ± 0.408 L) compared to females. Younger participants aged <30 years showed the highest CSApsoas, whereas participants aged 30-59 years showed the highest Vgluteus. Participants with higher BMI > 25 kg/m2 showed higher muscle CSA and volumes compared to subjects with lower BMI < 25 kg/m2. Vgluteus showed a strong correlation to body weight in both females and males. Conclusions: Deep learning-based models provide accurate 3D segmentation and quantification of skeletal muscle compartments from MR images in large cohort studies, thus offering a feasible method for skeletal muscle evaluation. The morphometric size characteristics of the psoas and gluteus muscles are dependent on gender and BMI. Deep learning enables accurate 3D segmentation and quantification of skeletal muscle in large MR imaging cohorts, providing a feasible tool for muscle evaluation. The morphometric characteristics of psoas and gluteus muscles are dependent on gender and BMI.