@InProceedings{Primus2018, title = {Frame-Based Classification of Operation Phases in Cataract Surgery Videos}, author = {Manfred Jürgen Primus and Doris Putzgruber-Adamitsch and Mario Taschwer and Bernd Münzer and Yosuf El-Shabrawi and Laszlo Böszörmenyi and Klaus Schöffmann}, booktitle = {MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 1)}, year = {2018}, address = {Berlin}, editor = {Klaus Schöffmann and Thanarat H. Chalidabhongse and Chong-Wah Ngo and Noel E. O´Connor and Supavadee Aramvith and Yo-Sung Ho and Moncef Gabbouj and Ahmed Elgammal}, month = {Januar}, pages = {241--253}, publisher = {Springer}, series = {LNCS}, volume = {10704}, abstract = {Cataract surgeries are frequently performed to correct a lens opacification of the human eye, which usually appears in the course of aging. These surgeries are conducted with the help of a microscope and are typically recorded on video for later inspection and educational purposes. However, post-hoc visual analysis of video recordings is cumbersome and time-consuming for surgeons if there is no navigation support, such as bookmarks to specific operation phases. To prepare the way for an automatic detection of operation phases in cataract surgery videos, we investigate the effectiveness of a deep convolutional neural network (CNN) to automatically assign video frames to operation phases, which can be regarded as a single-label multi-class classification problem. In absence of public datasets of cataract surgery videos, we provide a dataset of 21 videos of standardized cataract surgeries and use it to train and evaluate our CNN classifier. Experimental results display a mean F1-score of about 68% for frame-based operation phase classification, which can be further improved to 75% when considering temporal information of video frames in the CNN architecture.}, doi = {10.1007/978-3-319-73603-7_20}, url = {https://link.springer.com/chapter/10.1007/978-3-319-73603-7_20} }