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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.
It is performed with a higher radiation dose and larger dose of IV contrast, which helps to evaluate subtle areas of bowel inflammation. the slice thickness is 2.5 mm. This provides an excellent look at the large and small bowel enhancement and vasculature, and also the solid organs.
3 wrz 2020 · First this paper describe background of abdominal organs as well as modalities of imaging system. Then, we reviewed the techniques of deep learning for image segmentation, object detection, classification and other related tasks for multiorgan and single organ abdominal images.
Based on the rough localization results from the coarse localization network, a multi-scale attention network with shape constraints was developed to achieve accurate segmentation of the four abdominal organs.
25 paź 2023 · Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability in the...
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.
1 sty 2023 · Abstract. The accurate segmentation of multi-organ based on computed tomography (CT) images is important for the diagnosis of abdominal diseases, such as cancer staging, and for surgical planning, such as reducing damage to healthy tissues surrounding the target organ.
