用神经网络生成可模拟手术的3D前列腺模型，解决医疗数据难获取问题。
Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks
- 通过物理仿生模型+3D GAN生成多样化3D解剖数据
- 神经网络分割超声图像IoU优于传统方法，实现精准建模
- 适合医学影像生成、手术训练等需3D数据的场景
基于机器学习的手术规划与训练需要大量从医学影像重建的3D解剖模型，但真实患者数据获取受限于法律、伦理和技术挑战，尤其对成像对比度差的软组织器官如前列腺尤为困难。为此，本文提出一种自动化3D解剖数据生成工作流，利用物理器官模型获取数据，并采用3D生成对抗网络（GAN）生成可用于下游机器学习任务的3D模型集合。实验中使用由生物仿生水凝胶制成的前列腺模型，具备多区域成像对比度，用于模拟内窥镜手术。将该模型置于定制超声扫描仪中，记录术前术后图像。训练神经网络对超声图像进行分割，其交并比（IoU）优于传统非学习型计算机视觉方法。基于分割结果重建3D网格模型，并提供性能反馈。
原文摘要 · Abstract (English)
Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bottlenecks. Obtaining these data from real patients and during surgery is very demanding, if even possible, due to legal, ethical, and technical challenges. It is especially difficult for soft tissue organs with poor imaging contrast, such as the prostate. To overcome these challenges, we present a novel workflow for automated 3D anatomical data generation using data obtained from physical organ models. We additionally use a 3D Generative Adversarial Network (GAN) to obtain a manifold of 3D models useful for other downstream machine learning tasks that rely on 3D data. We demonstrate our workflow using an artificial prostate model made of biomimetic hydrogels with imaging contrast in multiple zones. This is used to physically simulate endoscopic surgery. For evaluation and 3D data generation, we place it into a customized ultrasound scanner that records the prostate before and after the procedure. A neural network is trained to segment the recorded ultrasound images, which outperforms conventional, non-learning-based computer vision techniques in terms of intersection over union (IoU). Based on the segmentations, a 3D mesh model is reconstructed, and performance feedback is provided.
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