From pixels to polygons: A survey of deep learning approaches for medical image-to-mesh reconstruction.
Fengming Lin, Arezoo Zakeri, Yidan Xue et al.
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In 30 seconds
This systematic review surveys deep learning methods for medical image-to-mesh reconstruction, categorizing approaches into four main types: template, statistical, generative, and implicit models. It evaluates these methods across various anatomical applications, highlighting their strengths and limitations. The review identifies key challenges, including topological correctness and geometric accuracy, essential for advancing computational medicine.
Key findings
- The review categorizes reconstruction methods into template, statistical, generative, and implicit models.
- Quantitative comparisons of methods are provided using standard metrics across various anatomical applications.
- Current challenges include ensuring topological correctness and geometric accuracy in mesh reconstruction.
Why it matters
Understanding the advancements in deep learning for medical image reconstruction is crucial for improving diagnostic and therapeutic techniques. This knowledge can enhance the accuracy of computational models used in clinical settings and research.