Segmentation of pelvic vessels in pediatric MRI using a patch-based deep learning approach

International audience<p>In this paper, we propose a patch-based deep learning ap-proach to segment pelvic vessels in 3D MRI images of pediatric patients.For a given T2 weighted MRI volume, a set of 2D axial patches areextracted using a limited number of user-selected landmarks. In orderto take into account the volumetric information, successive 2D axialpatches are combined together, producing a set of pseudo RGB colorimages. These RGB images are then used as input for a convolutionalneural network (CNN), pre-trained on the ImageNet dataset, which re-sults into both segmentation and vessel labeling as veins or arteries. Theproposed method is evaluated on 35 MRI volumes of pediatric patients,obtaining an average segmentation accuracy in terms of Average Sym-metric Surface Distance of ASSD = 0.89 ± 0.07 mm and Dice Index ofDC = 0.79 ± 0.02.</p

Beyond Quantity: Research with Subsymbolic AI

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately