Image-based recognition of surgical instruments by means of convolutional neural networks

This work presents a novel camera-based approach for the visual recognition of surgical instruments. In contrast to the state of the art, the presented approach works without any additional markers. The recognition is the first step for the implementation of tracking and tracing of instruments wherever they are visible and could be seen by camera systems. Recognition takes place at item number level. Surgical instruments that share the same article number also share the same functions. A distinction at this level of detail is sufficient for most clinical applications.

In this work, an image-based data set with over 6500 images is generated from 156 different surgical instruments. Forty-two images were acquired from each surgical instrument. The largest part is used to train convolutional neural networks (CNNs). The CNN is used as a classifier, where each class corresponds to an article number of the surgical instruments used. Only one surgical instrument exists per article number in the data set.

With a suitable amount of validation and test data, different CNN approaches are evaluated. The results show a recognition accuracy of up to 99.9% for the test data. To achieve these accuracies, an EfficientNet-B7 was used. It was also pre-trained on the ImageNet data set and then fine-tuned on the given data. This means that no weights were frozen during the training, but all layers were trained.

With recognition accuracies of up to 99.9% on a highly meaningful test data set, recognition of surgical instruments is suitable for many track and trace applications in the hospital. But the system has limitations: A homogeneous background and controlled lighting conditions are required. The detection of multiple instruments in one image in front of various backgrounds is part of future work.