Titlebook: Computational Methods and Clinical Applications for Spine Imaging; 5th International Wo Guoyan Zheng,Daniel Belavy,Shuo Li Conference proce

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IVD-Net: Intervertebral Disc Localization and Segmentation in MRI with a Multi-modal UNetl advances in medical imaging, IVD localization and segmentation are still manually performed, which is time-consuming and prone to errors. If, in addition, multi-modal imaging is considered, the burden imposed on disease assessments increases substantially. In this paper, we propose an architecture

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Intervertebral Disc Segmentation and Localization from Multi-modality MR Images with 2.5D Multi-scals. In this work, we proposed a robust automatic method based on 2.5D multi-scale fully convolutional network (FCN) and geometric constraint post-processing for IVD segmentation and localization on 3D multi-modality Magnetic Resonance (MR) scans. Firstly, we designed a 2.5D multi-scale FCN. And the e

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232689.jpg

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Stufen der Anordnung in Euklidischen R?umenWM). Segmenting GM is up?to a point a trade-off between accuracy and precision. We propose a new pipeline for GM-WM magnetic resonance (MR) image acquisition and segmentation. We report superior results as compared to the ones recently reported in the SC GM segmentation challenge and show even bette

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Stufengetriebe an Werkzeugmaschinenems could considerably improve the efficiency and objectivity of radiological practice by providing clinicians with image-based biomarkers calculated with minimal human input. However, such systems rarely achieve human-level performance, so extensive manual checking may be required. Their practical