Can surgical simulation be used to train detection and classification of neural networks?
Can surgical simulation be used to train detection and classification of neural networks?
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DOI：
10.1049/htl.2017.0064
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发表时间：
2017-10
影响因子：
2.1
通讯作者：
Stoyanov D
中科院分区：
文献类型：
--
作者：
Zisimopoulos O;Flouty E;Stacey M;Muscroft S;Giataganas P;Nehme J;Chow A;Stoyanov D
计算机辅助干预（CAI）旨在提高手术的有效性、准确性和可重复性，以改善手术结果。手术工具的存在和运动是CAI手术相位识别算法的关键信息输入。基于视觉的工具检测和识别方法是一种有吸引力的解决方案，可以设计为利用强大的深度学习范式，快速推进图像识别和分类。这种算法面临的挑战是用于训练的标记数据的可用性和质量。在这封信中，手术模拟被用于训练基于深度卷积神经网络和生成对抗网络的工具检测和分割。作者在白内障手术中经常遇到的工具类中实验了两种用于图像分割的网络架构。在对真实手术数据进行迁移学习之前，使用商用模拟器创建模拟白内障数据集用于训练模型。据作者所知，这是第一次尝试在模拟数据上训练手术器械检测的深度学习模型，同时展示了在真实数据上推广的有希望的结果。结果表明，模拟数据在训练CAI系统的高级分类方法方面具有一定的潜力。
Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors’ knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.