Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks
This addresses the problem of data scarcity for surgical training in medical imaging, particularly for soft tissues like the prostate, though it is incremental as it builds on existing GAN and segmentation techniques.
The paper tackles the bottleneck of obtaining 3D anatomical models for surgical planning by developing a workflow that uses physical organ models and a 3D GAN to generate data, demonstrating it with a prostate model where a neural network achieves higher IoU in segmentation than conventional methods.
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.