使用GAN和點雲資料的顱骨修復植入物設計的深度學習方法

顱骨成形術是一種重要的外科手術，主要用來修復因外傷或先前手術造成的顱骨缺損。此外，顱骨成形術還在保護大腦和維持外觀方面扮演著重要角色。在本研究中，我們提出了一個整合框架，結合生成對抗網路(GAN)、點雲資料以及深度學習模型如PointNet，以增強顱骨成形術的設計階段。我們的方法展現出能夠預測患者所需植入物的形狀和大小的潛力。此外，這個整合框架還能與最新的3D列印技術和電腦輔助設計(CAD)相結合，快速設計並生成適合患者的植入物。本研究的目標是實現智慧醫療，減輕醫療專業人員的工作負擔，提高手術精準度，縮短手術時間，提升患者的治療效果，並最終改善他們的康復過程。未來，我們將採用其他生成網路和點雲技術，進一步增強這個整合框架的功能。

Cranioplasty is a crucial surgical procedure, which aims at repairing cranial defects caused by injury or previous surgeries. In addition, it also plays an important role in protecting the brain and maintaining human appearances. In the study, we propose an integration framework, consisting of generative adversarial networks (GANs), point-cloud data, and deep learning models like PointNet, to enhance the design phase of cranial molding surgery. Our proposed approach demonstrates the potential to predict the shape and size of the implants needed for patients. Furthermore, our proposed integration framework can be combined with the latest 3D printing technology and computer-aided design (CAD) in order to rapidly design and generate well-fitted implants for patients. The study aims to achieve medical automation, reduce the overall workload of medical professionals, improve surgical accuracy, shorten surgery time, promote the treatment effects of patients, and finally facilitate their recovery experiences. In the future, we will adopt other generative networks and point-cloud technologies to enhance the whole function of an integration framework.