Instrument Segmentation
of a Surgical Robot
Data Preparation: To enhance the limited training data, a number of data augmentation techniques were applied to extract maximum information for the models to learn from.
Base Model Selection: Drawing from non-healthcare domain, we explored a number of Convolutional Neural Network architectures such as ResNet, Deep Lab V3, Fast RCNN, and UNet on various AI Platforms with Cost and speed to execution as key factors.
Model Training: We arrived at two different models one using Deeplab V3 (Tensor Flow) for accuracy of segmentation and one with Fast RCNN (PyTorch) to generate inferences to meet the real-time segmentation needs.
Hardware: A combination of Cloud hosted GPUs and Local GPU compute engines were used for data preparation, training and inference generation. With the elastic scaling capability of the hardware, we were able to tailor our computing costs to our needs.
Experiments: We tinkered with a number of base models including UNet and different AI platforms to arrive at an approach that allowed us to balance time/cost constraints and model accuracy.
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