Automatic tool detection from surgical imagery has a multitude of useful applications, such as real-time computer assistance for the surgeon. Using the successful residual network architecture, a system that can distinguish 21 different tools in cataract surgery videos is created. The videos are provided as part of the 2017 CATARACTS challenge and pose difficulties found in many real-world datasets, for example a strong class imbalance. The construction of the detection system is guided by a wide array of experiments that explore different design decisions.
@cite_19: The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed memory accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [, 2015], R-FCN [, 2016] and SSD [, 2015] systems, which we view as "meta-architectures" and trace out the speed accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Object detection is the problem of detecting possibly multiple objects of various classes in an image and returning their locations. This is somewhat related to the problem at hand: surgical tools must be detected in an image but the location is irrelevant. However, all state-of-the-art object detectors reviewed in @cite_19 need bounding box labels for their training, which are not available in the CATARACTS dataset. Consequently, existing object detection networks are unsuitable for this problem.