Quick look under the skin: Self-learning algorithms analyze medical imaging data


Imaging techniques enable a detailed look inside an organism. But interpreting the data is time-consuming and requires a great deal of experience. Artificial neural networks open up new possibilities: A TUM research teams lead by Rudolf Mößbauer Tenure Track Professor Bjoern Menze has developed self-learning algorithms to in future help analyze bioscientific image data. The software AIMOS (AI-based Mouse Organ Segmentation) is based on artificial neural networks that, like the human brain, are capable of learning. They require just seconds to interpret whole-body scans of mice and to segment and depict the organs in colors, instead of in various shades of gray. This facilitates the analysis considerably.

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Publication:
Oliver Schoppe, Chenchen Pan, Javier Coronel, Hongcheng Mai, Zhouyi Rong, Mihail Ivilinov Todorov, Annemarie Müskes, Fernando Navarro, Hongwei Li, Ali Ertürk, Bjoern H. Menze, “Deep learning-enabled multi-organ segmentation in whole-body mouse scans”, Nature Communications, 6.11.2020 – DOI: 10.1038/s41467-020-19449-7