Many medical decisions rely on histological analysis of tissue. The success of the analysis depends on the provided sample being from the to-be-classified tissue. 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


  • A. Patel, C. Otte, A. Schlaefer, D. Nir, S. Otte, T. Ngo, T. Loke, M. Winkler (2016). 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]