Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

Area of Science:

• Medical image computing
• Medical informatics
• Computational pathology

Background:

• Medical image computing is vital for software assistance in future diagnostics and navigated surgery.
• Current systems require enhanced automation, accuracy, reproducibility, and robustness.
• Integration into clinical workflows is essential for practical application.

Purpose of the Study:

• To explore the methodologies and future trends in medical image computing.
• To highlight the importance of quantitative image parameter extraction for reproducible diagnostics.
• To emphasize the role of image computing in advancing patient treatment and surgical interventions.

Main Methods:

• Adaptation and combination of methods from various scientific fields.
• Key methodologies include image segmentation, registration, analysis, modeling, simulation, and visualization.
• Emphasis on model-based image computing for prediction and risk analysis.

Main Results:

• Future trends include application-specific systems, enhanced computational models, and virtual reality training.
• Integration of diverse image computing methods and multimodal data (images, biosignals) is a key development.
• Advanced methods for 4D medical image computing are emerging.

Conclusions:

• Developing image analysis systems is a complex, interdisciplinary endeavor.
• Image computing offers novel insights into patient data.
• These methods hold significant potential to improve medical diagnostics and patient care.