{"id":28559,"date":"2023-09-27T14:43:51","date_gmt":"2023-09-27T12:43:51","guid":{"rendered":"https:\/\/www.risc-software.at\/referenzprojekte\/mimas_ai\/"},"modified":"2025-09-16T10:23:51","modified_gmt":"2025-09-16T08:23:51","slug":"mimas-ai","status":"publish","type":"project","link":"https:\/\/www.risc-software.at\/en\/referenceprojects\/mimas-ai\/","title":{"rendered":"Medical Image Processing, Modeling and Simulation based on Artificial Intelligence"},"content":{"rendered":"\n

The research area \u201cMedical Image Processing, Modeling and Simulation based on Artificial Intelligence\u201d (MIMAS.ai) covers highly dynamic topics. These topics are gaining importance in medical applications, mainly due to recent technological advances.<\/p>\n\n\n\n

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Medical image analysis and image segmentation<\/h2>\n\n\n\n

Medical image data are used for diagnosis, treatment planning, intervention monitoring, and documentation. Common modalities include 2D images such as X-rays, 3D scans such as CT or MRI, and even video sequences capturing temporal changes. To process these multimodal data, the Medical Informatics Research Unit develops methods for image analysis and segmentation. Using AI-based methods, the data are registered and segmented to extract patient-specific anatomical structures such as vessels, tissue, or skin. These segmented structures then form the basis for building medical models and simulations.<\/p>\n\n\n\n

However, the success of machine learning methods depends heavily on the quality and quantity of training data. Especially in medicine, suitable data are often missing or restricted due to privacy concerns. Therefore, research also focuses on generating ground truth data more efficiently. For example, CycleGANs are used to create synthetic training data, One Shot Learning expands datasets through augmentation, Transfer Learning applies existing models to similar problems, and Domain Adaptation adjusts models to new data distributions.<\/p>\n\n\n\n

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Deep Learning for the extraction of anatomic structures from patient cohorts, e.g., for abdominal aortic aneurysms.<\/em><\/p>\n<\/div>\n\n\n\n

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Learned Visual Representations of the BRATS dataset from Diffusion (left) and derived segmentations for only ten training slices (right).<\/em><\/p>\n<\/div>\n\n\n\n

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Deep Learning based segmentation of mandible, facial bone, teeth, mandibular canal and metal structures, to support patient specific implant design.<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Medical modeling and simulation<\/h2>\n\n\n\n

Modeling provides simplified representations of reality. In medicine, these models must be clinically relevant and based on available data. They range from 3D surface models to complex blood flow models. For example, virtual 3D patient avatars support diagnostics in burn medicine, chronic wound management, or forensics. Physicians can measure wound size, monitor healing, and objectively document treatment progress.<\/p>\n\n\n\n

Biomechanical simulations reproduce processes such as blood flow. Based on registered and segmented data, anatomical models are created, meshes are generated, and material properties are defined. From these models, quantitative features like vessel diameters or wall stresses are derived. Consequently, simulations help experts make better decisions. For instance, they can estimate the rupture risk of aneurysms or assess the effectiveness of stents. Moreover, simulators based on these models provide valuable training opportunities for physicians.<\/p>\n\n\n\n

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Deformed beam as an example of an ML-based surrogate model (Perceiver-IO) for solving elasticity equations. The dots correspond to the surrogate predictions, while the solid lines originate from the exact numerical simulation (FEM).<\/em><\/p>\n<\/div>\n\n\n\n

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Transfer of a burn wound from a 2D image onto a 3D patient model. The automatic wound localization and adaptation of the 3D model to the patient’s body shape enables a more objective estimation of the wound size and documentation of the healing process.<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Medical data analysis and prediction<\/h2>\n\n\n\n

Trust in machine learning applications is essential in medicine. Physicians and patients must rely on both the database and the prediction models. Therefore, MIMAS.ai emphasizes methods for validating data, interpreting predictions, and analyzing deviations.<\/p>\n\n\n\n

The Medical Informatics Research Unit develops complete data processing frameworks to support physicians in clinical practice. Use cases include:<\/p>\n\n\n\n