ArticleJournal of medical systems2024
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.
Article in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- AI-driven perioperative risk stratification and complication management in craniomaxillofacial surgery: current progress and future directions.Frontiers in surgery · 2026Review
- Review
- Current State of Knowledge Regarding the Treatment of Cranial Bone Defects: An Overview.Materials (Basel, Switzerland) · 2025Review
- A Comprehensive AI Framework for Superior Diagnosis, Cranial Reconstruction, and Implant Generation for Diverse Cranial Defects.Bioengineering (Basel, Switzerland) · 2025Article
- Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.Machine learning in medical imaging. MLMI (Workshop) · 2025Article
- Facial Masculinization Surgery Using Polyetheretherketone Alloplasty: Statistical Shape Modeling-based Implant Designs.Plastic and reconstructive surgery. Global open · 2024Article
- Creating high-resolution 3D cranial implant geometry using deep learning techniques.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.