Mainly, medical decisions rely on histological analysis of the tissue. The correct analysis depends on the provided sample of tissue which needs to be classified. Retrieving reliable tissue samples, e.g., by biopsy, is a challenging task. Hence, we study methods for biopsy guidance, i.e., using in-needle imaging techniques like optical coherence tomography.

Tissue Classification using Optical Coherence Tomography

References

  • A. Patel, C. Otte, A. Schlaefer, D. Nir, S. Otte, T. Ngo, T. Loke, M. Winkler (2016). MP34-14 Investigating the feasibility of optical coherence tomography to identify prostate cancer - an ex-vivo study. Presented at the Annual Meetig of American Urological Association (AUA) [BibTex]

  • C. Otte, S. Otte, L. Wittig, G. Hüttmann, C. Kugler, D. Drömann, A. Zell, A. Schlaefer (2014). Investigating Recurrent Neural Networks for OCT A-scan Based Tissue Analysis. Methods Inf Med. 53 (4), [doi] [BibTex]

  • L. Wittig, C. Otte, S. Otte, G. Hüttmann, D. Drömann, A. Schlaefer (2014). Tissue analysis of solitary pulmonary nodules using OCT A-Scan imaging needle probe. The European respiratory journal. [www] [BibTex]

  • C. Otte, S. Otte, L. Wittig, G. Hüttmann, D. Drömann, A. Schlaefer (2013). Identifizierung von Tumorgewebe in der Lunge mittels optischer Kohärenztomographie. [BibTex]

  • S. Otte, C. Otte, A. Schlaefer, L. Wittig, G. Huttmann, D. Drömann, A. Zell (2013). OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks. Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on 1-6. [doi] [www] [BibTex]