Sabrina Kletz presented the paper “Learning the Representation of Instrument Images in Laparoscopy Video” at the MIAR Workshop @ MICCAI 2019 in Shenzhen, China.

Authors: Sabrina Kletz, Klaus Schoeffmann, Heinrich Husslein

Abstract: Automatic recognition of instruments in laparoscopy videos poses many challenges that need to be addressed like identifying multiple instruments appearing in various representations and in different lighting conditions which in turn may be occluded by other instruments, tissue, blood or smoke. Considering these challenges it may be beneficial for recognition approaches that instrument frames are first detected in a sequence of video frames for further investigating only these frames. This pre-recognition step is also relevant for many other classification tasks in laparoscopy videos such as action recognition or adverse event analysis. In this work, we address the task of binary classification to recognize video frames as either instrument or non-instrument images. We examine convolutional neural network models to learn the representation of instrument frames in videos and take a closer look at learned activation patterns. For this task, GoogLeNet together with batch normalization is trained and validated using a publicly available dataset for instrument count classifications. We compare transfer learning with learning from scratch and evaluate on datasets from cholecystectomy and gynecology. The evaluation shows that fine-tuning a pre-trained model on the instrument and non-instrument images is much faster and more stable in learning than training a model from scratch.

Conference: 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 13–17, 2018, Shenzhen, China

Track: Medical Imaging and Augmented Reality (MIAR) Workshop @MICCAI