@misc{16612,
  keywords = {clustering, Computer Vision and Pattern Recognition (cs.CV), Unsupervised learning, human reproduction, medical videos},
  author = {Andrea Stor{\r a}s and Michael Riegler and Trine Haugen and Vajira Thambawita and Steven Hicks and Hugo Hammer and Radhika Kakulavarapu and P{\r a}l Halvorsen and Mette Stensen},
  title = {Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure},
  abstract = {The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.},
  year = {2022},
  journal = {NAIS: Symposium of the Norwegian AI Society},
  volume = {1650},
  pages = {1-11},
  publisher = {NAIS 2022},
  isbn = {978-3-031-17029-4},
  doi = {10.1007/978-3-031-17030-0_9},
}