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Deep Anatomy

The analysis of medical image data, such as computed tomography images or magnetic resonance imaging images , is central to various diagnostic and therapeutic procedures. Examples are early tumor detection, operation planning, minimally invasive interventions and monitoring the therapeutic process. The development of deep learning algorithms for specific medical image analysis tasks as well as suitable software applications will enable doctors in exploiting the full potential of modern imaging methods and to use them for the benefit of patients. This is exactly where DeepAnatomy comes in:

After intensive training in general and medicine-specific deep learning methods and the software and hardware resources used by Fraunhofer MEVIS in the form of lectures and participation in the Sartorius - Cell Instance Segmentation Challenge , we decided to develop seedRedLeaf to create and manage deep learning experiments. The web interface seedRedLeaf simplifies the use of RedLeaf, a Python library for deep learning developed by Fraunhofer MEVIS, and supports users in the configuration of network architectures provided by RedLeaf. As part of the development of seedRedLeaf, we also carried out extensive restructuring of key RedLeaf modules.

Learn more about the project on our website