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17 wrz 2022 · We introduce AbdomentNet, a deep neural network for the automated segmentation of abdominal organs on two-point Dixon MRI scans. A pre-processing pipeline enables to process MRI scans from different imaging studies, namely the German National Cohort, UK Biobank, and Kohorte im Raum Augsburg.
1 mar 2024 · Zhang et al. (2016) conducted coarse and fine lymph node segmentation based on two series-connected fully convolutional networks. Huang et al. (2021) proposed a modified deep residual U-Net model to predict the contour of abdominal organs and tissues.
17 wrz 2022 · Methods: We introduce AbdomentNet, a deep neural network for the automated segmentation of abdominal organs on two-point Dixon MRI scans. A pre-processing pipeline enables to process MRI scans from different imaging studies, namely the German National Cohort, UK Biobank, and Kohorte im Raum Augsburg.
1 gru 2023 · Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment decision-making.
Methods. A binary segmentation network is used for coarse localization, followed by a multi-scale attention network for the fine segmentation of liver, kidney, spleen, and pancreas.
1 mar 2021 · Highlights. •. Auto-context algorithm for CNN-based medical image segmentation. •. Self-supervised learning for effective contour attention. •. Superior performance on generalizing neural network. •. Introduction of N -fold cross-validation metric to analyze generalization performance. Abstract.
In this paper, we present a novel approach to multi-organ segmentation in abdominal CT examinations conducted across multiple centers, various phases, different vendors, and diverse disease conditions. This novel approach use deep learning (DL) and attention.
