Výsledky vyhledávání - "Moore, Ann Zenobia"

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Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation

Autor: Yu, Xin, Yang, Qi, Liu, Han, Lee, Ho Hin, Tang, Yucheng, Remedios, Lucas W., Kim, Michael E., Zhang, Rendong, Bao, Shunxing, Huo, Yuankai, Moore, Ann Zenobia, Ferrucci, Luigi, Landman, Bennett A.

2D single-slice abdominal computed tomography (CT) enables the assessment of body habitus and organ health with low radiation exposure. However, single-slice data necessitates the use of 2D networks for segmentation, but these networks often struggle

Externí odkaz: http://arxiv.org/abs/2406.12254

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Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization

Autor: Yu, Xin, Yang, Qi, Tang, Yucheng, Gao, Riqiang, Bao, Shunxing, Cai, Leon Y., Lee, Ho Hin, Huo, Yuankai, Moore, Ann Zenobia, Ferrucci, Luigi, Landman, Bennett A.

Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body co

Externí odkaz: http://arxiv.org/abs/2309.09392

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Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training

Autor: Yang, Qi, Yu, Xin, Lee, Ho Hin, Cai, Leon Y., Xu, Kaiwen, Bao, Shunxing, Huo, Yuankai, Moore, Ann Zenobia, Makrogiannis, Sokratis, Ferrucci, Luigi, Landman, Bennett A.

Objective: Thigh muscle group segmentation is important for assessment of muscle anatomy, metabolic disease and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging including manual annotation of indi

Externí odkaz: http://arxiv.org/abs/2212.00059

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Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

Autor: Yu, Xin, Yang, Qi, Tang, Yucheng, Gao, Riqiang, Bao, Shunxing, Cai, LeonY., Lee, Ho Hin, Huo, Yuankai, Moore, Ann Zenobia, Ferrucci, Luigi, Landman, Bennett A.

Publikováno v: Medical Image Computing and Computer Assisted Intervention MICCAI 2022, Cham, 2022, pp202,212

2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which are critical to quantitatively characterizing health relationships on aging. However, longitudinal analysis of body composition c

Externí odkaz: http://arxiv.org/abs/2209.14467

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Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation

Autor: Yu, Xin, Tang, Yucheng, Yang, Qi, Lee, Ho Hin, Gao, Riqiang, Bao, Shunxing, Moore, Ann Zenobia, Ferrucci, Luigi, Landman, Bennett A.

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice comp

Externí odkaz: http://arxiv.org/abs/2209.14217

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